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Facial Recognition System Using Local Binary
Patterns(LBP)
TS Vishnu Priya, G.Vinitha Sanchez, N.R.Raajan
School of Electrical & Electronics Engineering,
SASTRA Deemed University, Thanjavur, India
[email protected], [email protected], [email protected]
Abstract - There are several biometrics available like finger
print, iris identification etc. But Facial recognition or
detection is one of the biometric software applications that
can identify an particular individual in an digital image.
Face recognitions were used in many applications in the
field of banking, passport office etc. But the problem in the
face recognition is it cannot identify the person in the case
of identical twins. So the algorithm called local binary
patters were used to indentify the face in the case of
identical twins because the LBP can describe well about
the micro patterns present in the face.
Key Words - Micro Patterns, Pixels, Local Binary Patterns,
Histogram
I. INTRODUCTION Facial recognition is considered as a very tough challenge due
to variation in size, shape, color, and texture of human faces
and also there is no unique method to recognize the face
among the humans. Therefore in order to build a fully
automated system, a robust and efficient face recognition
method is required. The face recognition system consists of
recognizing the faces given as input with the data base
images[1]. There are several methods available to recognize
the face such as appearance based method, support vector
machine, hidden Markov model etc. This paper analysis a face
recognition based on local binary patterns which is
appearance based method.
II. EXISTING METHOD
In the existing system PCA method is used to recognize
the face[4]. Generally, PCA is used for reducing the dimension of the image. But one of the major problem with that is it cannot produce the complete information about the face therefore lose of information may occur in case of PCA algorithm. Also PCA algorithm cannot recognize face in case of identical twins.
III. PROPOSED METHOD
In order to overcome the above mentioned problems the
algorithm local binary pattern is proposed[2]. Since face
image is composed of several minute patterns this can be
efficiently identified by applying the local binary pattern
operator[5]. The local binary pattern operator is applied on the
given face image.
A. METHOD OF LOCAL BINARY PATTERNS(LBP):
In local binary pattern the input face is first converted into
the grey image and for that image the binary pattern is
calculated by comparing the center pixel with the surrounding
pixel.
Fig.1. Performance Of Local Binary Pattern (LBP) Operator
If the centre pixel is greater than that of the neighboring
pixel then it is denoted as 1 and if the neighboring pixel is
smaller than that of the centre pixel it is denoted as 0.This
should be done for each and every pixel so that we will get the
binary pattern.
Fig.2. Face image with pixels having uniform and non-uniform patterns
The local binary pattern is applied in the input image in
order to extract the important features of an image The
objective is to calculate the local binary pattern for each and
every pixels in an input image. Finally, the histogram is
calculated to find out the similarities of an given image.
In face recognition systems, the performance of the
algorithm is calculated by using the detection and false alarm
ratio .The common errors that occur in the face recognition
systems are,
False Negative: This error will occur because, the face is
not exactly recognized due to the poor ratio of detection
International Journal of Pure and Applied MathematicsVolume 119 No. 15 2018, 1895-1899ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/
1895
False Positive: This error will occur because, the non-face is
recognized as a face due to high ratio of false alarm.
The centre pixel co-ordinates are 𝑀𝑐 and 𝑁𝑐 ,then the co-
ordinates of the neighbor pixels are determined as follows,
𝑀𝑝 = 𝑅 cos 2 ∗ 3.14 ∗ 𝑝
𝑃 + 𝑀𝑐 (1)
𝑁𝑝 = 𝑅 sin 2 ∗ 3.14 ∗ 𝑝
𝑃 + 𝑁𝑐 (2)
𝑀𝑝𝑁𝑝 −Neighboring Pixels
𝑔𝑝 is the gray code of the neighbor pixels where p ranges
from 1 to (p-1), then the texture of an image can be calculated
as,
𝑇𝑥 = 𝑡 𝑔0 ,… . . 𝑔𝑝−1 (3)
Fig.3. Circular three different neighbor set for different values
Another possible way to find the texture of an image is by
subtracting the neighboring pixels values from the centre
pixels values which can given as follows, 𝑇𝑥 = 𝑡 𝑔0 − 𝑃𝑐 , … . . 𝑔𝑝−1 − 𝑃𝑐 (4)
𝑃𝑐 −Center Pixel
This means that that the neighboring(surrounding) pixel has
the high gray value when compared to the pixel at the center.
In that case the value is assigned as a one if not it is assigned
as a zero.
𝑇𝑥 = (𝑦 𝑔0 − 𝑃𝑐 , … . . 𝑦( 𝑔𝑝−1 − 𝑃𝑐)) (5)
𝑦 𝑛 = 0, 𝑛 < 01, 𝑛 ≥ 0
(6)
B. FEATURE VECTOR
After calculating the local Binary patterns for each and
every pixels the feature vector of the given image is
calculated. In order to get the efficient result the face is
divided into 𝑛2 region .In the below face image it is divided
into 𝑛2 i.e. 72 = 49 regions since n=7.
Fig.4. Face Image with n*n blocks
After dividing it into 𝑛2 region histogram is calculated for
each and individual pixel of an image, and then the histograms
for the region (𝑁𝑦 , 𝑁𝑧) can be calculated as,
𝐻𝐻𝑖 𝑁𝑦 , 𝑁𝑧 = 𝑋(𝐿𝐵𝑃 𝑦, 𝑧 = 𝐿 𝑖 )
𝑦 ,𝑧
(7)
𝑦 ∈
𝑅 + 1, … . .
𝑀
𝑘 ; 𝑁𝑦 = 1
𝑁𝑦 − 1 , … . . 𝑀 − 𝑅 ; 𝑁𝑦 = 𝑘
𝑁𝑦 − 1 , … . . 𝑁𝑦 𝑀
𝑘 ; 𝑒𝑙𝑠𝑒
(8)
𝑥 ∈
𝑅 + 1,… . .𝑁
𝑘 ; 𝑁𝑧 = 1
𝑁𝑧 − 1 , … . . 𝑀 − 𝑅 ; 𝑁𝑧 = 𝑘
𝑁𝑧 − 1 , … . . 𝑁𝑧 𝑁
𝑘 ; 𝑒𝑙𝑠𝑒
(9)
And finally, the histograms are concatenated in a single
vector feature.
yes
No
Fig.5. Block Diagram of Local Binary Pattern
Input Image
Face image is
divided into non
overlap blocks
Perform histogram
Histograms are
concatenated into
single feature
vector
More images?
Recognize
Face
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IV. STEPS TO BE PERFORMED FOR
FACE RECOGNITION There are the four important steps to be performed for face
recognition
1. FACE DETECTION:
This is the important initial step in the facial
recognition system, Performed to obtain pure facial images
with normalized intensity, uniform size and shape
2. FEATURE EXTRACTION:
Extracting the important Features in an face image is
done to obtain meaningful information that is useful to
identify the similarities between the different faces.
3.VERIFICATION:
The obtained Face image is then related with the
images available in the data base images. Once the obtained
image is matched with the data base image then it means that
the face is recognized otherwise it is not identified.
Table -1: Comparison of Various Methods and their performance
RESULTS AND DISCUSSIONS
INPUT IMAGE:
Here, the input image is loaded from the database and then it
is divided into 𝑛2 regions and local binary pattern is applied
for each region then histogram is calculated for each block
separately and finally it is concatenated into a single feature
vector.
OUTPUT IMAGE:
Here, the above output image will represent that the given
face image is recognized at the different directions
CONCLUSIONS
Since face image is composed of several minute patterns this
can be efficiently identified by applying the local binary
pattern operator. In this paper the more efficient facial
recognition technology is described that was successfully
applied to different analysis tasks, including face detection
and recognition, Iris detection, fingerprint recognition, and
problems related to expressions in face. This method will
extract the most important feature from the given image to
match the similarities between the different faces. Therefore
this local binary pattern method will work best when
compared to the other methods and also provides the efficient
result .
Methods Performance
Local Binary Pattern 89.3%
Principle Component
Analysis
64%
2D- Principle Component
Analysis
63.1%
Linear Discriminant
Analysis
55%
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