AUTOMATIC FACE DETECTION - Dr. Hussein S...

AUTOMATIC FACE DETECTION

Sheffield Hallam University

PREFACE

This report describes that the project work carried out in the Faculty of Arts, Computing, Engineering and Sciences at Sheffield Hallam University between June and September 2010.

The submission of the report is in accordance with the requirements for the award of the

degree of MSC Telecommunication & Electronics Engineering under the auspices of the

University.



ACKNOWLEDGEMENT

First of all I would like to thanks Almighty for all His kind blessings without which my work

has been knock-off. Also I would like to give the credit to the people who remember me in

their prayer for my achievements.

I take an opportunity to express my gratitude to Dr. HUSSEIN ABDUL RAHMAN, senior

faculty of Sheffield Hallam University for his constant support, valuable suggestions and

great ideas which enhance my level of performance. He also motivated me by providing apart

from his vast knowledge and sapience.

I would like to appreciate the faculty members management of university for providing me

great support through out my master course of education. Last but not the least, I would

honour to thanks my parents, brother and sisters for their encouragement and confidence on

my ability.

September, 2010 Khaleel Uddin Mohammed. Sheffield, UK M.sc Telecommunication & Electronics, 2010.

Department of Arts, Computing, Engineering and Sciences. Sheffield Hallam University.



Declaration I declare that, all the material contained in this document is solely my work except where it is indicated through proper citations and references. No part of this work has been submitted to any other university for a degree or any other qualification. September, 2010 Khaleel Uddin Mohammed. Sheffield, UK M.sc Telecommunication & Electronics, 2010. Department of Arts, Computing, Engineering and Sciences. Sheffield Hallam University.



ABSTRACT

Human face recognition systems have gained a considerable attention during last

decade due to its vast applications in the field of computer and advantages over previous

biometric methods. There are many applications with respect to security, sensitivity and

secrecy. Face detection is the most important and first step of recognition system.

Human face detection suffers from various challenges due to variation regarding

image conditions, size, resolution, poses and rotation. Its accurate and robust detection has

been a great task for the researchers. There exist various numbers of methods and techniques

for face detection but none can guarantee successful in all conditions for all kinds of faces

and images. Some methods are exhibiting good results in certain conditions and others are

good with different kinds of images. Face detection based on skin colour is found to be more

effective technique because of the properties of skin colour which is unique and can be easily

separated from the other objects present in the image and background.

Key Words: Face recognition, Face detection.



CONTENTS

Preface

Acknowledgement

Declaration

Abstract

Content

List of Figures 1 Introduction 1

1.1 Over view 2

1.2 Aims and Objective 2

1.3 Expected Output 2

1.4 What is Face Detection 2

1.4.1 Challenges 3

1.5 Thesis Structure 3

2 Literature Review 5

2.1 Background 6

2.2 Literature survey 7

2.3 Case studies 7

2.3.1 Case study-1 7

2.3.2 Case study-2 7

2.4 Methods of Face Detection 8

2.4.1 Knowledge Based Method 8

2.4.2 Feature Based Method 9

2.4.3 Template Based Method 10

2.4.4 Appearance Based method 11

2.5 Difference Between Face Detection and Face Recognition 11

2.5.1 Sensor Module 12

2.5.2 Face detection and Extraction of Facial Feature 12

2.5.3 Classification Module 12

2.5.4 System Database Module 12

3 Introduction to Image Processing 14

3.1 What is Image 15

3.2 What is Pixel 15

3.3 Image Processing 15



3.4 Image Enhancement 15

3.5 Image Restoration 16

3.6 Colour Image Processing 17

3.6.1 Radiance 19

3.6.2 Luminance 19

3.6.3 Brightness 19

3.6.4 Hue 19

3.6.5 Saturation 19

3.7 Image Segmentation 20

3.8 Edge Detection 20

3.9 Histogram Processing 21

3.10 Thresholding 22

4 MATLAB 24

4.1 MATLAB 25

4.2 MATLAB System 25

4.3 Advantages of MATLAB 26

4.4 Disadvantages of MATLAB 27

4.5 MATLAB Windows 27

4.6 Script Files 28

4.7 Arrays 29

4.8 Control Flows 30

4.8.1 For Loop 30

4.8.2 While Loop 31

4.8.3 If-Else-End Construction 31

4.8.4 Switch-Case Constructions 32

4.8.5 Try-Catch Block 32

4.9 Application areas of MATLAB 32

4.10 Key Features 33

4.11 MATLAB for Image Processing 33

4.12 Image Formats and Types Supported by MATLAB 34

4.12.1 Binary Images 34

4.12.2 Indexed Images 34

4.12.3 Grayscale Images 35



4.12.4 True Colour Images 35

5 Algorithms, Results and Comparisons 36

5.1 Colour Models 37

5.1.1 RGB Colour Space 37

5.1.2 HSI (Hue, Saturation, Intensity) Colour Space 38

5.1.3 YCrCb Colour Space 38

5.2 Algorithms 39

5.2.1 Skin Colour Based Face detection in RGB Colour Space 39

5.2.2 Skin Colour Based Face Detection in YCrCb Colour Space 39

5.2.3 Skin Colour Based Face Detection in HSI Colour Space 41

5.3 Proposed Algorithm For Face Detection 43

5.4 Experimental Results 44

6 Discussion, Conclusion and Future Work 49

6.1 Discussion 50

6.2 Conclusion 50

6.3 Future Work 51

7 References

8 Appendixes

8.1 MATLAB Code



LIST OF FIGURES

FIGURE NO. NAMES OF THE FIGURES PAGE NO

2.4.3.1 Face template for face localization 10

3.4.1 Eyes 16

3.4.2 X-ray 16

3.5.1 Image restoration 17

3.6.1 Refraction of light 18

3.6.2 Gray-scale 18

3.6.3 RGB image components 19

3.8.1 Edge detection 21

3.9.1 Histogram Parts 22

3.10.1 Thresholding 23

3.4.1 Matlab window 28

4.10.1 Image coordinate systems 33

4.10.2 Spatial coordinate system 34

5.1.1.1 RGB cube 38

5.2.3.1 HSV-cone 43

5.4.1 Input image-1 45

5.4.2 Image-1 segmented 45

5.4.3 Applying RGB to segmented image-1 45

5.4.4 Image-1 after edge detection 46

5.4.5 Binarlization 46

5.4.6 Noise removed by performing Morphological operation 46

5.4.7 Output of image-1(Face detected) 46

5.4.8 Input image-2 47

5.4.9 Image-2 segmented 47

5.4.10 Applying RGB to segmented image-2 47

5.4.11 Image-2 after edge detection 48

5.4.12 Binarlization 48

5.4.13 Noise removed by performing Morphological operations 48

5.4.14 Output of image-2(Face detected) 48



Chapter-1

Introduction



1.1 Over view:

Face detection demand is growing with a rapid speed because of its vast application in

the field of computer vision. For the implementation of different security methods as such

face tracking, face recognition and expression recognition and pose estimation all this

methods requires face detection algorithm. Though its got a lots of challenges variation in

poses, illumination, facial expression and occlusion the demand of face detection is still high

because of its non-intrusive nature. Because unlike other bio-metric, face detection method

gives better identification for security and best result than any other bio-metric methods.

Face detection is not only the first step of any automatic face recognition system but also for

human computer interaction system, generic object recognition and detection and many

surveillance systems.

1.2 Aims and Objectives:

Aim of the thesis is to evaluate the face detection process using the all three colour spaces

so that advantages and application of all three colour space can club together into a single

proposed algorithm. The objectives of the thesis is

• First is to understand the different methods of face detection methods

available under which various techniques are used.

• After the analysis of different method a suitable technique is proposed which

is more accurate and have high detection ratio.

• Compare the proposed technique of face detection with the other techniques.

1.3 Expected outcome:

The expected output of the automatic face detection method based on skin colour is

the same image which is given as input to the system but with boundaries across the faces

present in the image showing the exact location and size of the image. This boundaries across

the face may be of any shape like square and circle but in the algorithm we have proposed,

accurate shapes (squares or circles) are not consider but it completely depends on the shape

of the face so that no redundant information comes into the picture.

1.4 What is Face Detection:

Face detection is defined as finding the number of face present in an image and

locating their position and size. Generally the process of face detection is partition in two



steps in the first stem the whole image is scanned to find the region (features) that can be

identified as face, skin colour is the most common among all. And the next is localisation

which gives more accurate estimation of size and position of the faces. Face detection is the

first step of human identification and recognition system.

1.4.1 Challenges: Face detection method faces are various challenges such as pose, imaging

condition.

Pose: The image of a face vary due to relative camera-face pose and some facial

feature becomes partially or completely occluded.

Image orientation: face images directly vary for different rotation about the camera’s

optical axis. Image orientation directly affects the angle of the face.

Illumination: This problem is mainly due to lighting, makes a larger difference with

the same face as compare to difference within different faces while comparing

(Javidi.Bahram, 2002).

Occlusion: Sometime, faces in the image are occluded with other objects such as

moustaches, beard, optical lenses and other types of object make it very difficult to find

accurate image.

Facial expression: Some persons may have different expression at different times,

this also contributes to challenge in face detection.

Face size: Size of the face also make difficult to automate a system for face detection

and recognition (Muhammed Tariq Mahmood, 2002).

Image condition: This problem includes factors such as intensity, resolution, camera

lighting, background, characteristics of image capturing device and distance between camera

and person, plays an important role in the process of face detection.

1.5 Thesis structure:

Chapter 1: Introduction to face detection covers the aims and objective of face

detection and also the problem faced in achieving effective face detection.

Chapter 2: Briefly describe the methods of face detection and explain the difference

between the face detection and face recognition.

Chapter 3: It gives the clear picture of image processing and the different fields of

image processing.



Chapter 4: This chapter deals with the MATLAB tools and image formats supported

of MATLAB. And also describe the control flows of MATLAB.

Chapter 5: In this chapter three colour spaces and their algorithms along with the

proposed algorithm are discussed in depth.

Chapter 6: The final chapter if this thesis discussions and future work on the face

detection method is carried out.

Chapter 7: This chapter composes of code of the algorithm proposed on skin colour

based face detection.



Chapter-2

Literature Review



2.1 Back ground:

From the advent of cell phones in the electronics history till today there is continuous

development in the technology used in the cell phones. Modern cell phones have so many

features such as integrated cameras, high resolution display, multiple communications

interfaces, and processing power and many more equal to at least, to a mid-1990’s PC. Cell

phones are most widely distributed in terms of range of users, geographical distribution than

any other class of computers. This makes them an attractive development platform for many

image processing and computer vision algorithms. Among this algorithms face detection is

early step in many computer vision systems, including Video communication, Super video

compression, Face recognition, Video surveillance, Responsive user interface, Video-

augmented speech understanding, Intelligent autofocus etc.

Video communication: A high quality image with fixed bandwidth can be obtained

by compressing the detected image at lower compression rate than the background.

Super video compression: Once a face is detected, it can be matched with some pre-

recorded faces of the presenter and then the chosen face index transmitted and displayed,

resulting in an satisfactory video experience using very small bandwidth.

Face recognition: After detecting a face it is compared with a database of known

faces, and then an appropriate response can be made when a face s recognised.

Video surveillance: Detected faces can be tracked in order to find out when a person

has entered an area of interest.

Responsive user interface: A tool may perform some pre-programmed actions when a

face is detected.

Video-augmented speech understanding: Speech understanding can be made easier by

detecting and then tracking face movements to determine which of several similar-sounding

phonemes was actually being pronounced.

Intelligent autofocus: Once a face is detected, a camera’s focus can be used to bring

the face into improved focus (Jon A.Web, 2010).



2.2 Literature Survey:

2.3 Case studies:

The case studies contain the over view of two methods of face detection using the same

technique (skin colour) with two different approaches one uses the guassian mixture model

and other uses the morphological operations.

2.3.1 Case study 1:

Face detection in colour images: Face detection is divided into three parts human skin

segmentation to describe the probable regions corresponding to face, adaptive shape analysis

to separate human faces from initial segmentation and view based face detection to further

identify the location of each human face. Human colour (skin colour) which is important

feature of human face and produces information which is invariant to face detection. Feature

space in which skin colour cluster tightly and reside remotely to background colour, is used

to effectively exploit skin colour for face detection.

YCbCr colour space is the common for face detection since it is uniform and more

important is that it separate luminance and chrominance. As many studies reveals that

chrominance an luminance must be separated from each other so that there will be no effect

of illumination (insensitive to illumination). Dispersion of skin colour is prototyped with a

Guassian mixture model (WenmiaoLu and ShaohuaSun, 2000).

2.3.2 Case study 2:

Face Detection Using Colour Based Segmentation and Morphological Processing:

This case study describe a algorithm that uses colour based segmentation and some

morphological processing. The colour based segmentation. The colour based segmentation

takes the advantage of patterns developed in only two colour spaces i.e HSI and YCrCb,

instead of three colour spaces, followed by the morphological operations and a template

matching. Different ethnic group have different degrees of the melanin and pigmentation, the

range of colours that human facial skin takes on is clearly a subspace of the total colour

space.

The steps defined for this algorithm is as follows:

1. Select an image as input.

2. Apply HSV and YCbCr colour mode.

3. Calculate skin ranges for H, Cb and Cr.



4. Obtain colour segmented image.

5. Apply open operation on the segmented image.

6. Check for holes if any. And find width and height of the region.

7. Find correlation of face with template.

8. Region consider as image ( Dr. Arti Khaparde, Sowmya Reddy.Y Swetha

Ravipudi, 2007).

2.4 Methods of face detection:

Automatically detecting human face has played a vital and challenging task in

computer vision research. The consequence of this is its vast application, since it is the first of

towards intelligent vision-based human computer interaction and it the first stage of

automatic face recognition system. Detecting human face had been a difficult and challenging

task since it involves the many problem such as image size, face size, face angle, pose,

features extraction, type and many more. For this there are number of methods and

algorithms are developed each has its own significance. The four categories of face detection

algorithms are as follows

2.4.1 Knowledge based method:

This method relies completely on the simples rules which describe the features of the

face and the relation (for example distance) between them such as there exist a uniform

intensity in the central region of the face and also eyes are symmetric to each other. The

search algorithms guided by the rules are applied to find the target face [Yang, Ming-Hsuan,

2001]. The other features may be chin, nose and eyebrows. A hierarchical approach which

studies the face at various resolution can be used. At top level faces of the people present in

the image are taken out roughly and in the low level facial features are extracted thus

separating the face with the non-face region accordingly depending up on the rules defined on

the facial features.

Yang and Huang introduce a knowledge based method also known as top down

method in 1994. This method is divided in three stages. In the first stage high rules are

defined and in the second stage edge detection techniques is done using histogram and

equalization methods.



Knowledge based method is good for the frontal image thus finds difficulty in

detecting faces in taken at different poses or head orientation. This is because it is not easy to

translate human knowledge about the rules into definite rules. In practice this method of face

detection is not often used.

2.4.2 Features based method:

The central theme of this method is to find the features of the face that are invariant or

not affected by any factors such as variation in lighting conditions, pose and others. The

different features that can be used are eyes, eyebrows, mouth, nose, face shape, texture and

skin colour which are extracted using the derivatives filters, edge detectors, morphological

operations or thresholding. Statistical analysis is done to get the relationship and verify the

presence of faces.

Skin colour is one of the powerful and effective features to detect the face since it has

the colour thus colour scene segmentation is computationally fast. The other important of

skin colour as facial feature to detect face is it is robust to changes in the view point, complex

background and shading to partial occlusion. Though geographically, people of different

country has different skin colour. This difference lies in their intensity not on their

chrominance. The unique texture of skin colour is used to separate it from different object

present in the image. Each pixel of the image is labelled according to its similarity with the

skin colour and then the regions are made as face if it contains skin colour pixel above a

particular number(threshold) and non-face for the region is it contain less than the threshold.

This colour based method is sensitive to light, occlusion, adjacent faces ant also if the

features are corrupted severely. And it is also find difficulty in detecting face in complex

background.

Feature detection method is completely based on detecting feature of the face. Edge

detection one of the technique used to detect the features and followed by grouping the edges

and other is to use blob and streak instead of edges. For example in the face model eyes, nose

and cheeks bones are represented as two dark blobs and three light blobs. For the facial

features such as outlines of eyebrows and lips, streak model is used. To improve the detection

accuracy multi features methods that combines several features is used. Firstly to detect the

face by using features such as skin colour, texture and shape and lastly to verify these using

features such as nose, hair and eyebrows.



2.4.3 Template based method:

In this method a large number of templates of faces are stored into the system. The

input image from which the faces is to be detected is processed by making head outline which

is elliptical (roughly) is made using edge detector and filters. After that a contours (line

joining the facial features) of the facial features are drawn which gives the information about

face and features geometry. After all, the correlation between the predefined stored templates

and the features extracted from the input image is computed to know whether there exist a

face in the image or not. As it is a correlation process input images with different poses,

scales and shapes are sensitive parameters. To overcome this sensitive parameters deformable

template method have been proposed which allow to scale, translate and rotate by using

elastic model which model the geometry of the face. Parameters of the model can be not only

the shape but also intensity information of facial features. P.Sinha [1994] utilises a group of

images invariants to describe the face pattern. The different parts of the face have different

brightness for example the brightness of the eyes and nose changes while illumination. This

methods calculated the pair wise ratios of brightness and projected directions. A face is

localised by satisfying all conditions for dark and bright regions. The figure below is the

enhance figure that have 23 defined relation for a face template.

Fig 2.4.3.1 Face template for face localization (P.Sinha, 1994)



It is difficult to define all templates of the faces thus make it unsuccessful for different

poses and illumination problems.

2.4.4 Appearance-based methods:

Similar to template based method templates are learned by computer system in the

form of training algorithms which classify the regions into two types of classes there are face

or non-face class. The face class shows the images that contain the face images and the non-

face class shows the images other than the images of face class that is anything that is not a

face. For learning face and non-face classes machine learning algorithm is used. All

techniques used under appearance based methods have common steps there are classification

of face and non face classes, pre-processing, learning and post processing of the images.

Statistical analysis and machine learning techniques are used to determine the probability

distribution function of the pixel brightness patterns of images of the two classes. The names

of some famous appearance based methods are hidden Markov, neural networks, Adaboost,

decision tree, eigen faces, LDA and decision tree model.

2.5 Difference between face detection and face recognition:

The immense application of face recognition, its has became a popular area of

research in field of computer vision. Due to its ability of problem solving, it has gained

attention of not only computer researchers but also psychologist ant neuroscientist. The

increase in commercial and law enforcement application commanding personal

authentication for people’s surveillance, transaction security, the name face recognition

becomes famous. The other bio-metrics techniques which are also more reliable such as

fingerprints and iris recognition which are intrusive and they need the assistance of user

unlike face recognition which is non-intrusive as it is grounded completely on images

recorded from a distant by a camera. And the most important thing is that it does not required

user assistant and works even when the user has no idea about the existence of the face

recognition system. Face gives much information about the person in personal identification

system than the finger and iris does.

Face recognition works in two modes there are face identification and face

verification. Face identification works by matching the input image with the images stored in

the computer/database and in face verification the user gives an identity as an input to the

system and the machine accepts or reject depending on matching process performed on input



image and the images stored in the database with this specific identity (Filareti Tsalakanidou ,

Sotiris Malassiotis 2 and Michael G. Strintzis, 2009).

A face recognition system comprises of four modules there are as follows:

2.5.1 Sensor module:

This module captures face images, depending upon the requirement the image

acquisition device can be black and white camera or the colour camera.

2.5.2 Face detection and extraction of facial features:

The input images are scanned to detect the presence of face and window the location

containing only the face area. After finding the exact location and size of the face, irrelevant

information (neck, hair, shoulders and background) are cast aside which will help in

extracting of facial features used by face classifier to verify the identity of unknown face.

2.5.3 Classification Module:

In this module, comparison of the template or images extracted from the face

detection process and the a large set of stored images or templates in the database is done and

generate matching scores which gives the information about how identical the two images

are. A decision making module then perform either verification or identification on the basis

of matching score. For face verification, the matching score is compared with predefined

threshold and on the basis of comparison the user is accepted or rejected. For the face

identification, a set of matching scores between the extracted templates and the images or

templates of enrolled users is calculated. If the matching score is good, indicates that the user

face (unknown) is more similar to the face in the template stored in the database than any

other templates. To conform that unknown face is actually the one stored in the database,

matching score is compared with the predefined threshold (in the database).

2.5.4 System database module:

Database module is used to stack the templates of the entered (enrolled) users and also

solely responsible for enrolling users in the database of face recognition system. Sensor

module reads the images of his/her face and they are stored in database for training the

classifier, the stored images are termed as gallery images. If feasible these images are stored

in more than one session depending upon the face variations such as hairstyle, beard, make-

up, etc this enhance the performance of face recognition system. The training algorithm



determines the different values of threshold. The training algorithm is, for instance given

some images as input, then the features are extracted as process defined in the above step and

the template that provides much similar information as that of the input image are generated

and this are called training. This training algorithm depends on the method of face

recognition. The aim of the training is to encode the most discriminative quality of a user

based on the classifier selected, and to find out the values of the different thresholds (Filareti

Tsalakanidou , Sotiris Malassiotis 2 and Michael G. Strintzis, 2009).



Chapter-3

Introduction to Image Processing



3.1 What is Image:

A Two dimensional function, f(x, y) with x and y are spatial coordinates is termed as

Image and the amplitude of function f(x, y) at any point of coordinates (x, y) is called as

intensity of the image or the gray level. If the value of x, y and the amplitude of f(x, y) are

discrete than the image is said to be digital image.

3.2 What is Pixel:

Pixel is the basic, smallest discrete component of the image that can be controlled.

Each pixel has particular location and value. They are also called as pels, picture elements,

image elements.

3.3 Image Processing:

The term Digital image processing refers to processing of two dimensional data

(image) and the term digital image refers to an array of real or complex number represented

by finite number of bits.

Digital image processing has broad range of applications, such as remote sensing via

satellites and spacecrafts, medical processing, radar and acoustic image processing and

robotics.

There are various applications of image processing comes out of different fields of it.

The different fields of image processing are as followed.

Image enchancement

Image restoration.

Colour image processing

Image segmentation

Edge detection

Histogram processing

3.4 Image Enhancement:

According to David Lindsay image enhancement is : All that makes the difference

whether one sees darkness through the light or brightness through the shadows.

The main objective of enhancement is to improve the quality of images for human

visualisation and analysis that is making it(images) more suitable for a specific application.

Increasing contrast, removing blurring and noise are some of the operation of enhancement.



Image enhancement approaches are classified into two categories: spatial domain method and

frequency domain method. Spatial domain techniques are based on the location of pixels in

the image and frequency domain techniques are based on changing the Fourier transform of

an image. There are various techniques of image enhancement some of them are contrast

stretching, Power-law transformation, Gray level slicing, Bit-plane slicing etc. (Rafael C.

Gonzalez and Richard E. Woods, 2001)

Fig 3.4.1 Eyes (Zia-ur Rahman, 2001)

Fig 3.4.2 X-ray (Zia-ur Rahman, 2001)

3.5 Image Restoration:

Immanuel Kant: ‘’Things which we see are not by themselves what we see....It

remains completely unknown to us what the object may be by themselves and apart from the

receptivity of our senses. We know nothing but our manner of perceiving them.’’

The difference between the image enhancement and image restoration is that image

enhancement is subjective process whereas image restoration is largely objective process.

Image restoration is used to fix the color, the contrast, the tone and to repair damages which

is caused due to non-linear filtering produced by sensors, geometry distortion correction,



noise filtering and environmental limitation. Thus restoration techniques are pointed toward

modelling the degradation and putting on the inverse process in order to retrieve the original

image. the following figures will shows the best result of image restoration.

Fig 3.5.1 Image restoration (ozlouisville)

3.6 Colour Image Processing:

Pablo Picasso: ‘’for a long time I limited myself to one colour-as a form of discipline.

The use of colour in image processing is encouraged by two factors since colour often

help in object identification and extraction from the images is one factor and the another

factor is human eye can distinguish thousand of colour shades and intensities and only about

two dozen shades of gray. Colour image processing is divided into two major parts: full

colour image processing and pseudo colour image processing. In full colour processing the

important thing to be noticed is from where the images are acquired (from the colour TV

camera or colour scanner). And in pseudo colour processing, the problem is of assigning

colour to a particular range of intensities. Since many years full colour image processing is

not used however from a decade, colour sensors and colour processing hardware have

available at reasonable prices resulting in utilization of broad range of application such as

publishing, visualization, and the internet.[ Rafael C. Gonzalez, Richard E. Woods, 2001]



In colour image processing, the colour spectrum is divided into six broad region:

violet, blue, green, yellow, orange and red which are formed by passing white light through

the optical prism as shown below. The colour that human perceive in an object are

determined by the nature of the light reflected from the object. The object that reflects light

that is poised in all visible wavelengths looks white to the user an the object that favours

reflectance in a confined area of visible spectrum exhibits some shades of colour. For

instance green object reflects light in the range of 500nm to 570nm (wavelength) whilst

observing most of the energy at other wavelength.

Fig 3.6.1 Refraction of light (Ian Flitcroft, 2007)

Characteristics of light plays an important role in science of colour. The lights that

comes out of the black and white television is achromatic light that is white, grey and white

are achromatic colour and the light that comes out from this colour is achromatic light (as

shown below) ranges from 400 to 700nm. Radiance, luminance and brightness are used to

define the quality of achromatic light.

Fig 3.6.2 Gray-scale (Tomás Castelazo, 2006)



3.6.1 Radiance: The net amount of energy that comes out from the light source.

3.6.2 Luminance: It is the amount of energy an observer perceives from a light source.

3.6.3 Brightness: brightness is a subjective signifier and in practical it is not possible to

measure the brightness.

Red, green and blue are the three primary colours separated from the other on the

basis of the sensing capability of cones present in human eye. Due to the absorption property

of the human eye, colours are seen as variable combination of red, green and blue which are

called as primary colour. All colours resulting from the combination of primary colour are

termed as secondary colours such as magenta, yellow, cyan and red. The characteristic that is

used to separate the colours from each other are brightness, hue and saturation.

3.6.4 Hue:

Hue represent dominant colour as perceived by the observer, it is property associated

with the colour that has dominant wavelength in a mixture of light waves.

3.6.5 Saturation:

It gives the information about the purity of the colour or the amount of white colour

mixed with hue. For instance pink is a combination of red and white colours is less saturated.

That the degree of saturation and the amount of white colour added are inversely proportional

to each other.

The combination of Hue and saturation are termed as chromaticity and hence all

colours are characterized by their brightness and chromaticity.

Fig 3.6.3 RGB image components (Gonzalez and Woods, 1992)



3.7 Image segmentation:

Segmentation is a process of subdividing an image into constituents regions or objects

so that the minute details of the image are read or analyzed. The objective of segmentation is

to change or modify the representation of the image into meaningful and simplified way (easy

to analyzed). This process comes into picture when the object of interest is to detect or

identified from a set of objects and terminate immediately after the object is isolated from the

rest, there is no point in carrying out the segmentation process past the level of detail required

to identify the object of interest.

Like that of other fields of image processing, image segmentation also got numerous

application some of them are: Diagnosis, study of anatomical structure, object location in

satellite images, military imaging etc. Generally image segmentation algorithms are based on

two properties of intensity values: discontinuity and similarity. In the category of

discontinuity, images are segmented based on abrupt changes in intensity. And in the second

category, images are partition based on the similarity exists with the predefined criteria.

Based upon the two categories of image segmentation there are various of approaches are

analyzed among them the most wanted are edge detection and thresholding.

3.8 Edge Detection:

Its is the most approach for detecting meaningful discontinuities in grey level. The

set of connected pixels that lies on the boundary between two regions are termed as edge or it

is ability of the pixels to measure the gray-level transitions in a meaningful way and the edge

detection is to detect this pixels. Images are blurred due to bad sampling and imperfections of

image acquisition. Thus the edges are more closely modelled as ‘’ ramplike ‘’ profile as

shown below. The slope of the ramp and the blurring in the image are inversely proportional

to each other. Any pixels that lies on the ramp line are said be edge point. The length of the

line which depends on scope of the line is determined by the degree of blurring. Thus, it is

found that sharp edges tends to be thin and the blurred edges tends to be thick.

In the figure shown below first and second derivative of ramp is calculated. The first

derivative is positive at the point of transition into and out of the ramp (from the left to right)

and constant throughout the ramp line. Second derivative is positive only at the instant of

transition associated with the black side, negative only at the instant of transition associated

with the light side and zero throughout the ramp line. Thus first derivative is used to identify

the presence of an edge at a point in an image and the second derivative is used to conclude



whether an edge pixel lies on dark or light side of an image. Second derivative has two

attributes, for every edge in an image it produces two values and the other is zero crossing

property. The zero crossing, an imaginary line crossing zero at the midpoint of two values of

second derivative, joining the positive and negative values.

Fig 3.8.1 Edge detection (Shehrzad Qureshi, 2009)

3.9 Histogram processing:

Its a most common technique of image enhancement. It is a vertical bar chart that

gives the information about the distribution of set of data. when a table with a large set of

measurement is given, Histogram is used to organised and display the data in more simple

and user friendly format. A Histogram will make it effortless to determine where the bulk of

values falls in a measurement scale, and how much variation there exists between them.

The following shows some of the situation where Histogram is needed:

Summarize large data sets graphically

Compare process results with specification limits

Communicate information graphically.

Help to assist in decision making.

(http://www.scribd.com/doc/6811944/Histogram-VG)

As shown below, Histogram consists of five parts:

1. Title: The title briefly describes the information contained in the Histogram.

2. Horizontal or X-Axis: The horizontal or X-axis shows you the scale of

values into which the measurements fit.



3. Bars: The bars have two important characteristics—height and width. The

height represents the number of times the values within an interval occurred. The width

represents the length of the interval covered by the bar.

4. Vertical or Y-Axis: The Y-axis is the scale that demonstrates the number

of times the values within an interval occurred which is termed as frequency.

5. Legend: The legend provides additional information for example where the

data came from and how the measurements were gathered.

Fig 3.9.1 Histogram Parts

3.10 Thresholding:

Thresholding enjoys central position in applications of image segmentation because of

its intuitive property and ease of implementation. It tries to identify an object from its

background. Intensity is the property that pixels in a region can share with each other. So, a

normal way to segment such a regions is through separation of light and dark regions which

is termed as thresholding. Its produces binary image from grey-level by bending all pixels

below some threshold to zero and all pixels about that threshold to one.[11]

If g(x,y) is a thresholding version of image f(x, y) at some global threshold T.



Fig 3.10.1 Thresholding (Lindsay come, 2005)

The problem with the thresholding is that it does not consider the relationship

between the pixels but consider only the intensity property of the image. Thus there is no

guarantee pixels identified by the thresholding process is continuous. No information is given

if an extra pixels are included or disregarded within the given region. There are various

techniques for thresholding among them the simplest is global thresholding where image

histogram is partition by using a single global threshold T. Then segmentation process is

started to scan the image pixel by pixel and labelling each as object or background based on

the gray level of the pixel is lesser or greater than the value of T. In global thresholding only

a single threshold is defined and result with a problem of changes in illumination across the

scene causes some parts to be brighter in light and some parts darker in shadow. There is one

more technique of thresholding based on several variables which is called as multispectral

thresholding.



Chapter-4

MATLAB



4.1 MATLAB:

MATLAB (is the abbreviation of MATrix LABoratory) is special purpose computer

program to perform engineering and scientific calculation. It is used for simple mathematical

calculation such as mathematics, computation as well as to solve complex problems such as

for understanding and teaching engineering concepts, data analysis, exploration,

visualisation, prototyping, analysing and simulating of actual electrical and power systems.

MATLAB is a high-level language and interactive environment chosen to perform

computationally intensive tasks faster than with traditional programming languages such as

C, C++, and Fortran ( The MathWorks, Inc., 2010).

MATLAB can be used in wide range of application such as signal processing, image

processing, control design , biological computation, finance modelling and analysis. The

most interesting feature of MATLAB is that integration of MATLAB code with other

languages and applications. In 1970s MATLAB was produced as a technical tool, because of

its huge application it has expanded and now used for car design, aerospace and many

advance researches.

Toolboxes, one of the important feature of MATLAB. Toolbox allow the user to

learn and apply specialized technology and hence preferred as a tool of choice by many

researchers. This feature of MATLAB is a collection of m-files that stretch the MATLAB to

workout for particular families of problem. Toolbox are available for the area: control

system, image processing, fuzzy logic, signal processing and many more (Khairul Anuar

Ishak, 2008).

4.2 MATLAB system:

MATLAB system consist of five main sections:

1. The MATLAB language: MATLAB language is high-level matrix language

that includes control flow statement, data structures, functions and object oriented

programming features. It allows both programming in small which produces output quickly

and also programming in large and complex programs.

2. The MATLAB working environment: these are the set of tolls and facilities

which includes facilities for managing the variables in workspace and importing and

exporting data also developing, debugging and profiling m-files.



3. Handle graphics: It is graphics system which includes commands for image

processing, animation two-dimensional and three dimensional data visualization also it

includes low level command that helps in fully customizing the appearance of graphics.

4. The MATLAB mathematical function library: it is large collection of simple

algorithms such as sine, cosine and complex arithmetic to more advanced functions such as

Bessel functions, FFT (Fast Fourier Transform), eigen values.

5. The MATLAB Application Program Interface(API): This section of

MATLAB allows to write C and Fortran programs that interact with MATLAB (Khairul

Anuar Ishak, 2008).

4.3 Advantages of MATLAB: MATLAB has many advantages over conventional computer

language, some of them are

1. User friendly: Programs written in MATLAB can be easy modified with the

built-in integrated development environment and corrected with the MATLAB debugger and

hence used for rapid prototyping of new program.

2. Platform independence: MATLAB runs on many computer operating systems

thus providing a high range of platform independence.

3. Predefined Function: MATLAB has a large numbers of functions that are

predefined that gives tested and pre-packaged solutions to many basic technical problems

such as mean, square root, standard deviation etc. Also one more special purpose tool of

MATLAB to solve the complex problems in specific areas are Toolboxes.

4. Device-Independent Plotting: MATLAB has integral plotting and imaging

commands which is not found in other languages of computer. This plots and images can be

visualized on any graphical output device installed on computer on which MATLAB is

present.

5. Graphical User Interface: MATLAB comprises of tool that permit the user to

construct a graphical user interface. With this powerful mechanism of MATLAB, even an

inexperience user can design data analysis program.

6. MATLAB Compiler: Flexibility of MATLAB lies in compiling MATLAB

program into a device-independent p-code after that interpreting the p-code instruction at

runtime which effects the execution of program since the code id interpreted instead of

compiled (Khairul Anuar Ishak, 2008).



4.4 MATLAB has two disadvantages:

The execution of program takes more time than the compiled language since it is an

interpreted language. This effect can be reduce by proper structuring the program and by the

use of MATLAB compiler to compile the final program before distribution and general

use.The cost of MATLAB is very high which is five to ten times greater than C or Fortran

compiler. But luckily student edition of MATLAB, an inexpensive, same as full edition of

MATLAB is available.

4.5 MATLAB Windows:

Windows used in MATLAB and their basics functions are as shown below.

WINDOWS FUNCTIONS

Command Issue command to MATLAB for processing

Command history Running history of prior command issued in

the command window

Launch pad Tree layout for access to tools, demos and

documentation

Current directory GUI for directory and file manipulation in

MATLAB

Help GUI for finding and viewing on-line

documentation

Workspace GUI for viewing, loading and saving

MATLAB variables

Array editor GUI for modifying the content of MATLAB

variables

Editor/debugger Text editor and debugger for MATLAB text

files.

MATLAB has Command History Window, Command Window and Work Space

Window. When the MATLAB software is open the following screen opens which has

Command Window on the right side, Work Space Window on the top-left and Command

History Window on the bottom left.



Fig 3.4.1 Matlab window (Robert Evans, 2009)

Solutions of simple problems can be evaluated using the command window which is

fast and efficient. As the number of commands increases or when there is need to change the

value of any variable, typing at MATLAB prompt becomes tiresome. For this MATLAB

provides simple text file, this files are called script files or M-files. It is called as script file

because MATLAB simply reads from the script found in the file and M-file as these file must

end with the extension ’.m’, for instance, example1.m.

4.6 Script M-Files:

MATLAB not only supports many built-in function such as logarithmic,

trigonometry, complex and exponential but also the user-defined functions which worked as

conveniently as built-in functions. Lookfor command is used to find the functions that are

relevant to the particular application. For example exponential gives a list of functions that

deal with exponent.

lookfor exponential

exp - Exponential.

expint - Exponential integral function.

expm - Matrix exponential.



expmdemo - Matrix Exponentials

expmdemo1 - Matrix exponential via Pade approximation.

expmdemo2 - Matrix exponential via Taylor series.

Note : help command is used to know the working and utilisation of the function or

command.

The built-in functions are store as image file so there function and computational

details are not readily accessible. The functions that are implemented in M-files thus there

computational details and code can be read.(Williams J.Palm, 2009)

4.7 Arrays:

Operation that involve analysis of single number is called scalars, is the basis of

mathematics. But there exists many occasion where it is needed to perform operation on more

than one number at a time (repeated scalar). To overcome this problem, MATLAB defines

arrays operation.

Arrays are create by starting with left bracket followed by the values separated by

space or commas and then close the array with right bracket.

(source bruce little field )

Cell array and structures are the two data types of MATLAB. These are used to group

dissimilar but related arrays into a single variable. This allow much help in data management

as group of related data can be organised and accessed through a cell array or structure. The

elements of structure can be accessed using the command field and is different from cell

arrays which are accessed using standard array indexing operation.

(source :William j palm)

Example 1: A 2 X 2 cell array K containing the location name, date, air temperature

and the water temperature at three different point in the pond at the same time

K (1, 1) = {‘ Swift pond’};

K(1, 2) = {‘July 28, 1987 ’};



K(2, 1) = {‘[30 40 50]’};

K(2, 2) = {[35, 45, 55; 65,75, 85; 95, 15, 25]};

Example 2: A structure array to create database of student containing the information

about the types Student name, student ID, email address and marks. Each type of data is field.

Structure array of students

Student-1 Student-2

Name: Nigel Name: David

Student ID: 1903956 Student ID: 1903896

Email: [email protected] Email: [email protected]

Marks: 78, 56, 67 Marks: 45, 69,72

(William J. Palm, 2009)

4.8 Control Flow:

For simple problem interactive mode of MATLAB is very useful but for the complex

problem it requires a script file which are termed as program and writing such a file is called

programming. The value of MATLAB is greatly increased by the use of decision making

(Control flow) functions in it programs. Decision making functions are very useful since it

lets past computations influence future operations. MATLAB provides five decision making

or control flow structures there are Loops, While Loops, If-Else-End, Switch-Case

construction and Try-Catch blocks.

4.8.1 For Loop:

It allows a group of commands to be repeated for a fixed number of times. The

general form of For Loops is

For x = array

(command)

End

Example:

For a = 2 : 4 :100

b = k ^2

end

The variable a is assigned the value of 2 (initially), then b is calculated as b = k^2. In

each cycle a is incremented by 4 and calculates b until a exceeds 100.



4.8.2 While Loop:

As against the For Loop that calculates group of command a fixed number of times,

while loop calculates group of commands indefinite number of times. The general form of

while loop is

While expression

(command)

End

Example:

a = 5;

while a = 0

disp(a)

a = a – 2;

end

The variable a takes the values 3, 1, -1, -3........because the condition x¬= 0 is always

satisfied. Thus the loop never terminates then to terminate this process Ctrl-C is pressed.

4.8.3 If-Else-End Construction:

This decision making statement is used where the sequence of command required to

be conditionally evaluated based on a relational test. The general form of if-else-end

statement is

If expression

(command evaluated if true)

else

(command evaluated if true)

End

Example:

>> sweet = 5;

>> cost = sweet * 10

cost = 50

>> if sweet > 3 % 40% discount

cost = (1-40/100) * cost;

end

>> cost



cost = 40

4.8.4 Switch-Case Constructions:

These statement is used when a large number of commands are to be evaluated

depending on repeated use of an equality test with single common argument. The general

form is

Switch expression

Case test_expression1

(command)

Case { test_expression2, test_expression3, test_expression4}

(command)

Otherwise

(command3)

End

4.8.5 Try-Catch Block:

It provides user-controlled error-trapping capabilities. Thus it help in capturing error

found by MATLAB which gives the ability to control the error to the user. There are

generally expressed as

Try

(command1)

Catch

(command2)

End

4.9 Application areas of MATLAB:

• Technical computing

• Control system

• FPGA design

• Mechatronics

• Image and video processing

• Embedded systems

• Digital signal processing

• Computational finance



4.10 Key Features:

� High-level language for technical computing

� Development environment for managing code, files, and data

� Interactive tools for iterative exploration, design, and problem solving

� Mathematical functions for linear algebra, statistics, Fourier analysis, filtering,

optimization, and numerical integration

� 2-D and 3-D graphics functions for visualizing data

� Tools for building custom graphical user interfaces

� Functions for integrating MATLAB based algorithms with external

applications and languages, such as C, C++, Fortran, Java, COM, and Microsoft Excel.

4.11 MATLAB for Image Processing:

MATLAB stores images in the form of two dimensional matrices such that each

element in the matrix represent a single pixel of the image. True colour images uses three

dimension matrices in which first plane represent intensity of red pixels, second plane

represent intensity of green and the third plane represent blue.

Pixel coordinate is used to give information about the location in an image. In pixel

coordinate system image is addressed as framework of discrete elements arranged in top to

bottom and right to left. . Pixel coordinate system are expressed as (r, c).

Fig 4.10.1 Image coordinate systems (Mathworks)

(http://www.mathworks.com/access/helpdesk/help/toolbox/images/brcu_al-1.html)

Spatial coordinate is other type of coordinate system in which locations in a image are

expressed in terms of x and y, spatial coordinate are expressed as (x, y). The spatial



coordinate system is continuous where as pixel coordinate is discrete.

Fig 4.10.2 Spatial coordinate system (Mathworks)

4.12 Image Formats and Types Supported by MATLAB:

The following image formats are supported by MATLAB:

• BMP (Bitmap)

• HDF (Hubble Deep Field)

• JPEG (Joint Photographic Experts Group)

• PCX (Picture Exchange Format )

• TIFF (Tagged Image File)

• XWB

MATLAB supports four types of images

1. Binary images

2. Indexed Images

3. Grayscale Images

4. Truecolour Images

4.12.1 Binary Images: Its an array (matrices) of logical 0s and 1s reads as black and white

respectively. To test whether a given array is binary (logical), command islogical (k) is used.

If the output is 1 then it is binary otherwise not.

4.12.2 Indexed Images: These are also called as pseudo-colour images, it comprises of array

and colourmap matrix. The pixel values in the array are direct indices into a colour-map.

Array is represented as X and colour map which is an array of m-by-3 with a floating point

in the range of[0 1] is represented as map. The m-rows of colour-map (map) gives the

information about the values of components (red, green and blue) of a single colour. An

indexed image uses direct mapping of pixel values to colourmap values. The matrix map of



indexed images are m-3 array of class double containing floating point values in the range of

[0, 1].

4.12.3 Grayscale Images: Grayscale images are the data matrix are also called as intensity

ranges from [0, 255] and [0, 65535] for class unit8 and unit16 respectively. Grayscale images

are stored as individual matrix.

4.12.4 True Colour Images: These type of images are also called as RGB images,

represented as m-n-3 array of colour pixel. That is each pixel is triplet corresponding to red,

green and blue component. An true colour images regarded as a tack of three gray scale

images that when fed into the red, green and blue inputs of colour monitor, produces a colour

image on the screen. The components of true colour image are each of 8-bits which implies

true colour images are stored as 24-bits in graphical file format.[9]

Operation: MATLAB

command:

Convert between intensity/indexed/RGB format to binary

format. dither()

Convert between intensity format to indexed format. gray2ind()

Convert between indexed format to intensity format. ind2gray()

Convert between indexed format to RGB format. ind2rgb()

Convert a regular matrix to intensity format by scaling. mat2gray()

Convert between RGB format to intensity format. rgb2gray()

Convert between RGB format to indexed format. rgb2ind()



Chapter-5

Algorithms, Results and

Comparisons



5 Algorithms Result and Comparisons:

There are various algorithms developed for skin colour based face detection using

different colour models such as RGB colour model, HSV colour model and YCrCb. The

algorithm we proposed is the combination of all three or one can say part of all three colour

models. For clear understanding of the proposed algorithm, the algorithms of three colour

models and their characteristics must be clear.

5.1 Colour Models:

The purpose of colour model is to ease the specification of colour in some standard. In

general a colour model is a specification of a coordinate system, subspace present in colour

space is used to represent each colour by a single point.

5.1.1 RGB Colour Space:

RGB model is based on Cartesian coordinate system which is cube aid is shown

below. The cube has RGB values at the three corners, colours like cyan, magenta and yellow

and other three corners, black is at the origin and white is at farthest corner from the origin.

The gray scale lies on the line joining black and white. They are termed as ‘’additive

primaries’’ because the colours are added together to produce the desired colour. Due to high

correlation between colour components: red, green and blue also each of the component is

subjected to luminance effect from the light intensity of the environment, thus this suffers

dissatisfaction from many image processing applications. Also practically, this model is not

well suitable for describing colours in terms of human interpretation.(Sanjay Kr. Singh, D. S.

Chauhan, Mayank Vatsa, Richa Singh, 2003).



Fig 5.1.1.1 RGB cube (Paul Bourke, 1995)

5.1.2 HSI (Hue, Saturation, Intensity) Colour Space:

This colour model is ideal for hardware implementation. Practical human

interpretation is describe in term of Hue Saturation and Intensity (Brightness). HSI model

separates the intensity component from the colour carrying information, thus is an ideal tool

for developing image processing algorithm based on colour description that are natural and

intuitive to human. HSI is based on cylindrical coordinate, hue(H) is represented with an

angle 0, varying from 0 to 360, saturation(S) corresponds to radius varying from 0 to 1 and

finally the intensity(I) varies along the z axis with 0 being black and 1 being white.

When the value of S is 0, colour is a gray value of intensity. When the value of S is 1,

colour is on the boundary of tope cone base. The greater the saturation, the farther the colour

is from white / gray/ black. Chancing the value of colour will change the colour from red

which is at 00 to green which is at 1200, blue at 2400, and black to red at 3600. And when I =

0, the colour is black and therefore H is undefined. By varying the value of I, a colour can be

made darker or lighter. Shaded of the colour comes out when the value of I is adjusted while

maintaining S = 1.

5.1.3 YCrCb Colour Space:

This colour space has been defined to meet the increasing demand of digital

algorithms in handling video information and has become the widely used colour space in

digital videos. It has three components, two of them is of chrominance and one is of



luminance. This model comes in the family of television transmission of colour space along

with YUC and YIQ which are designed for analog space for PAL and NTSC systems.[14]

YCrCb colour model was developed in order to allow to transmit colour information

onto the television sets keeping in mind that existing black and white television sets continue

to display the images in tones of grey. And got the characteristic of isolating luminance and

colour information, thus used in many applications such as compression.

5.2 Algorithms:

5.2.1 Skin Colour Based Face detection in RGB Colour Space:

Skin colour is the most important facial feature and is unique because of its colour

ingredients. Skin colour pixels can be easily detected using normalized colour histogram

which is future normalized for any change in intensity on dividing of luminance. And thus

RGB vector is converted in to an [r, g ] vector of normalized colour which in turns offers a

fast mean of skin detection. This gives skin colour region which localizes face. As stated

early that RGB suffers from luminance effect but it still able to allows us to remove certain

colours that are clearly out of range of the normal skin colour. After studying and analysing

the various thresholds in RGB space it was found that following rule works well in removing

some redundant pixels which are labelled as non-face.[14]

0.836G – 14 < B <0.836G + 44 => Skin

0.79G – 67 < B < 0.78G + 42 => Skin

5.2.2 Skin Colour Based Face Detection in YCrCb Colour Space:

it has been found that skin colour pixels belonging to region exhibit similar Cb and Cr

values also it is found that skin colour model based on the Cr and Cb values can provide good

coverage of human races.

YCrCb signals which are created from the corresponding gamma-adjusted RGB source

using two defined constants KB and KR.

After the gamma correction and prior to scaling and offsets the YCrCb signals are

termed as Y1,PB and PR which are defined as shown below.



Here, the prime ′ symbols mean gamma correction is being used, thus R′, G′ and B′

nominally range from 0 to 1, with 0 representing the minimum intensity (e.g., for display of

the color black) and 1 the maximum (e.g., for display of the colour white). From this the

resulting luminance signal (Y) value will then have a nominal range from 0 to 1. And the

chrominance value (PB and PB) values will then have a nominal range from -0.5 to +0.5.

For representing signals in digital form, rounding off and scaling and offsets are

added. For instance scaling and offset applied to the Y′ component per specification results in

the value of 16 for black and the value of 235 for white when using an 8-bit representation.

The standard has 8-bit digitized versions of CB and CR scaled to a different range of 16 to

240. As a result, rescaling by the fraction (235-16)/(240-16) = 219/224 is mandatory when

doing colour matrixing or processing in YCbCr space, leading quantization distortions when

the successive processing is not executed using higher bit depths.

The form of Y′CbCr that was defined for standard-definition television use in

the ITU-R BT.601 (formerly CCIR 601) standard for use with digital component video is

derived from the corresponding RGB space as follows:

KB = 0.114

KR = 0.299

From the above constants and formulas, the following can be derived for ITU-R

BT.601. Analog YPbPr from analogue R'G'B' is derived as follows:

Y/ = 0.299 * R/ + 0.587 * G/ + 0.114 * B/

PB = -0.168739 * R/ - 0.331264 * G/ + 0.5 * B/

PR = 0.5 * R/ - 0.418688 * G/ - 0.81312 * B/

Digital Y′CbCr (8 bits per sample) is derived from analogue R'G'B' as follows:

Y/ = 16 + (65.481 * R/ + 128.553 * G/ + 24.966 * B/ )

PB = 128 + (-37.797 * R/ - 74.203 * G/ + 112.0 * B/

PR = 128 + (112.0 * R/ - 93.786 * G/ - 18.214 * B/



Note that the resultant signals range from 16 to 235; the values from 0 to 15 are

called footroom, while the values from 236 to 255 are called headroom.

Alternatively, digital Y′CbCr is derived from digital Rd Gd Bd (8 bits per sample)

according to the following equations:

Y/ = 16 + (65.481 * Rd + 128.553 * Gd + 24.966 * Bd ) / 256

PB = 128 + (-37.797 * Rd - 74.203 * Gd + 112.0 * B/d) / 256

PR = 128 + (112.0 * Rd - 93.786 * Gd - 18.214 * Bd ) /256

Y/ = 16 + (0.255 * Rd + 0.5021 * Gd + 0.0975 * Bd )

PB = 128 + (-0.148 * Rd – 0.368 * Gd + 0.439 * B/d)

PR = 128 + (0.439 * Rd – 0.368 * Gd - 0.071 * Bd )

The above equation are used in the coding part of the proposed algorithm. Cr and Cb

are used instead of PB and PR. After studying and experimenting with various thresholds,

finally found that the best results were found by using the following decree and otherwise

assume that it is NOT skin and may be removed from further consideration (Michael Padilla

and Zihong Fan, 2003).

102 < Cb < 128 => Skin

5.2.3 Skin Colour Based Face Detection in HSI Colour Space:

The HSV colour space is more intuitive and provides colour information in a manner

same as the humans think of colours and how artists typically mix colours. Hue along with

saturation provides the useful selective information regarding skin. The range of Hue for non

face region is found to be as shown below and otherwise it is assumed to be skin. HSI colour

model is represented using three

19 < H < 240 => Not Skin

HSI colour space is defined by using the RGB as scale as transformation from RGB

space to and HSI space (Gonzales and woods)

H = 3600 – α / 3600 if b > g

= α / 3600 other wise

Where α = cos-1 2R – G – B / (R2 + G2 + B2 – RG – GB – BR )1/2

S = 1-[min (R, G, B)] / I

I = (R + G + B) / 3



Colours are defined given an RGB colour cube. The colour cube is oriented such that

the value component is oriented along the gray axis, which runs from black to white.

HSV are defined purely with reference to some RGB space, they are not absolute

colour spaces: to specify a colour precisely requires reporting not only HSV values, but also

the characteristics of the RGB space they are based on, including the gamma correction in

use.

Computer science offers a few poorer cousins to these perceptual spaces that may also

turn up in software interface, such as HSV . They are easy mathematical transformations of

RGB, and they seem to be perceptual systems because they make use of the hue–

lightness/value–saturation terminology. But take a close look; don’t be fooled. Perceptual

colour dimensions are poorly scaled by the colour specifications that are provided in these

and some other systems. For example, saturation and lightness are confounded, so a

saturation scale may also contain a wide range of lightness (for example, it may progress

from white to green which is a combination of both lightness and saturation). Likewise, hue

and lightness are confounded so, for example, a saturated yellow and saturated blue may be

designated as the same ‘lightness’ but have wide differences in perceived lightness. These

flaws make the systems difficult to use to control the look of a colour scheme in a systematic

manner. If much tweaking is required to achieve the desired effect, the system offers little

benefit over grappling with raw specifications in RGB (Cynthia A. Brewer, 1999).



Fig 5.2.3.1:HSV-cone

5.3 Proposed Algorithm For Face Detection:

In the proposed algorithm, it is assumed that by combining the detected region from

all the three algorithm, skin region is extracted. After combining the three algorithms and

extracting skin colour, following this face is detected by edges using edge detection which

draws boundary across the face region. It is something like if one algorithm detect the skin

colour for an image and others two algorithm fails and gives the false result, even then the

face is extracted by using the combination algorithm. This supposition of combining

algorithm, is based on the basic idea of Venn diagram from set theory. Suppose the result

from RGB colour is ‘’A’’, results from YCbCr colour space is ‘’B’’ and the result from HSI

colour space is ‘’C’’ and if any of the result contains a skin image then the union of the three

will surely be a skin image. This can be understood by the following example: : let, A = {a, b,

c, d}, B = {b, e} and C = {b, f}. If “a” = “skin image” then skin image is present only in A

and the next two are failed in extracting the skin colour then the union of the three U = (A U

B U C) ={a, b, c, d, e, f} hence this algorithm can detect skin colour or else if we take ‘’e’’ or

‘’f’’ as skin image then also the union (U) will have skin image in it also this is applicable if



all the three (A, B, C) has skin image. Hence in all sense the skin region are going to be

detected (Sanjay Kr. Singh, D. S. Chauhan, Mayank Vatsa, Richa Singh, 2003).

After extracting the skin region, facial features such as eyes, nose and mouth are

extracted. The image obtained after applying skin colour information is given to binarization

i.e., it is transformed to gray-scale image and then to a binary image by applying suitable

threshold. This is done to eradicate the hue and saturation values and consider only the

luminance part. This luminance part is then transformed to binary image with some threshold

because the features we want to consider further for face extraction are darker than the

background colours. Opening and closing operations are performed to filter out noise. The

morphological close operation is nothing but a dilation followed by an erosion, and the

morphological open operation is an erosion followed by dilation, these operations are

performed to remove holes.

5.4 Experimental Results:

In this section, images resulted from the proposed algorithm are shown with two

examples (Input-1 and Input-2). Each example has six images as outputs: segmented image,

applying RGB to segmented image, image after edge detection, binarlization, noise removed

by performing morphological operation, output image (Face detected).

Segmentation: Segmentation is the main part of the algorithm, gives the minute description of

the pixel. So that the suitable operations can be performed. The operation performed in the

proposed algorithm are based on the three colour spaces used.

Applying RGB: Convert image resulted from segmentation to colour image.

Edge detection: This is done in order to trace the edges and separate the regions containing

face with that of non-face.

Binarlization: It converts the image into binary image.

Morphological operation: As explained earlier, it perform open and close operation to remove

noise.

Output image: Shows the outcome of the algorithm.



Chapter-6

Discussion, conclusion and Future

Work



6.1 Discussion:

Face detection using image skin colour methods have shown reasonable advancement

in the area of computer vision and biometric image analysis. The combination with machine

learning techniques has resulted more accurate and efficient.

During current study we revealed the area of face detection. A comprehensive

survey is carried out to understand the problems and opportunities in context of computer

vision and machine learning. The area demands a lot of research to achieve a higher level of

accuracy and to gain computational efficiency. Different approaches and techniques are

discussed. The pros and corn helped to understand the problem and leads to proper method

selection according to the nature of image database.

Skin colour based face detection proposed in this thesis is the most effective than that

of the face detection methods based on other facial features. Any combination of facial

features along with skin colour uplift the performance to high level. As explained there exits

various methods and in each method there exist various techniques, the best method based on

the experiments results is consider as features based method in that the best technique is

using skin colour.

The three different colour space RGB, HSV and YCrCb are briefly discussed and from

the discussion it is concluded that using all three colour spaces will be more advantageous.

Thus the thesis covers all the factors that contribute to the effectiveness of the face detection

process from all three colour space available.

6.2 Conclusion:

From the work carried out on this thesis, it is concluded that it is very difficult and still a

challenging task to build up an automatic face detection method that is works effectively

in all situation whether the image consists of different face poses, bad image condition or

image effected by illumination. Researchers are still struggling hard to find out the

perfect solution to this complicated problem. But their exist, the different face detection

method to overcome different problems of face detection. For instance to overcome the

problem of lighting (illumination) RGB colour space is not used instead HSV or YCrCb

are used. Some methods or techniques shows good result for particular problem or

application and others shows good result for different application. So it may not be

insane to say, that researchers still have lot of work to do under face detection.



6.3 Future work:

The goal, robust face detection and recognition, is still far. The case study discussed

during the current work is interesting for further research and development activities. Due to

a variations and a huge number of images is perfect platform for experimenting algorithms.

Face detection based on skin colour studied during this thesis work can be

implemented to other biometric systems. For instance systems involved detection and

recognition of iris, ear, palm, signature, and finger prints etc need comprehensive image

analysis. These techniques are very helpful while analysing patterns and classification.

As discussed in chapter two the face detection has been a challenging task due to

number of factors like poses, image condition and illumination. Most of the exiting

methods and algorithms are unable to provide solutions to these problems of variations. A

considerable attention is required to develop efficient methods that can work with and

overcome to such problems. The research in this area is very actively going on and the

current study will help for laying solid foundation for it. Most of the researchers are

performing experiments by using different colour spaces and even different combination of

this colour spaces as this thesis used all three colour spaces in performing experiments.

For more effective and accurate face detection other facial features apart from the skin

colour such as nose, mouth and eyes can be used which really enhance the performance. Any

one of the facial feature or the combination of any two or all can be consider to increase the

effectiveness of the face detection process (K.V Kale, S.C Mehrotra and RR Manza, 1999).

Case study-2 describe in this thesis uses the morphological operations such as opening

and closing which help in reducing the noise after the segmentation is carried out. This

morphological operations are also utilized in the code of this thesis, which also reduces the

noise level up to satisfactory level.

It is found that MATLAB is the only effective tool or the platform of carrying outface

detection process, thus this becomes a problem for many students and young researches as

there is no comprehensive platform for performing experiments. All have to rely on

MATLAB or one has to develop code from other language like C, C++ or JAVA. The

complex mathematics and statistical equations demand hung amount of time and efforts.

There are few C++ libraries provided by some research groups free of cost for further

research in the area of image processing but still they are insufficient for building up a

platform for experiments in the area of face detection and recognition.



Chapter-7

References



References:

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and Applications. New York, NY, USA: Marcel Dekker Incorporated, 2002.

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pdf

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pr.html



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and information technology. New Delhi, India: I.K Innternational

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Discriminating. Available:

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Chapter-8

Appendixes



8.1 MATLAB Code:

% function segment=skin(I);

close all;

clear all;

clc;

[file, pathname] = uigetfile('*.jpg','Load Image');

cd(pathname);

I=imread(file);

% I=imcrop(I);

imwrite(I,'org.jpg');

I=imresize(I,[255 255]);

qq=I;

imshow(I);

% I=imresize(I,[256,256]);

I=double(I);

[hue,s,v]=rgb2hsv(I);

cb = 0.148* I(:,:,1) - 0.291* I(:,:,2) + 0.439 * I(:,:,3) + 128;

cr = 0.439 * I(:,:,1) - 0.368 * I(:,:,2) -0.071 * I(:,:,3) + 128;

[w h]=size(I(:,:,1));

for i=1:w

for j=1:h

if 140<=cr(i,j) & cr(i,j)<=165 & 140<=cb(i,j) & cb(i,j)<=195 & 0.01<=hue(i,j)

& hue(i,j)<=0.1

segment(i,j)=1;

else

segment(i,j)=0;

end

end

end



imshow(segment);

title('segmented');

im(:,:,1)=I(:,:,1).*segment;

im(:,:,2)=I(:,:,2).*segment;

im(:,:,3)=I(:,:,3).*segment;

figure;imshow(uint8(im));

title('colour-Segmented image');

sk=im;

im=rgb2gray(im);

BWa = edge(im,'sobel');

figure,imshow(BWa);

title('segmentated with edge detection,gray-scale image ');

im=imresize(im,[255 255]);

X = imcomplement(im);

figure,imshow(X);

title('binarlizatiom');

bwa = bwareaopen(X,90);

se = strel('disk',5);

bww = imclose(bwa,se);

bwb = imclose(bww,se);

figure,imshow(bwb);

title('After morphological operations');

BWa = edge(bwb,'sobel');



figure,imshow(BWa);

dim = size(BWa)

col = round(dim(2)/2)-90;

row = min(find(BWa(:,col)))

boundary = bwtraceboundary(BWa,[row, col],'N');

hold on;

imshow(qq)

BW_filled = imfill(BWa,'holes');

boundaries = bwboundaries(BW_filled);

[m n]=size(boundaries);

w=(m+n);

for k=1:(w-1)

b = boundaries{k};

plot(b(:,2),b(:,1),'g','LineWidth',1);

title('Face detected');

end

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