WELCOME 1 First International Conference on Intelligent Infrastructure, 1-2 December, 2012.

WELCOME

1First International Conference on Intelligent Infrastructure, 1-2 December, 2012

Image Registration of North-East Indian (NEI) Face Database

Goutam Majumder#, Rajib Debnath#, Mrinal Kanti Bhowmik#, Debotosh Bhattacharjee*, Mita Nasipuri*

#Department of Computer Science and Engineering, Tripura University (A Central University), Suryamaninagar - 799022,

Tripura, India*Department of Computer Science and Engineering,

Jadavpur University, Kolkata - 700032, West Bengal, India


Presented By:

Goutam MajumderDepartment of Computer Science & Engineering

Tripura University (A Central University)Suryamaninagar – 799022, Tripura, India


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Introduction

• Image Registration is the process of transforming different sets of data into one coordinate system.

• It is a fundamental task in image processing used to match two or more images, taken at different times, from different viewpoints or from different sensors [1].

• Registration can be performed in two categories: either automatically or manually.

First International Conference on Intelligent Infrastructure, 1-2 December, 2012

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Introduction

• Image Registration is widely used in remote sensing, medical imaging, computer vision and many other applications.

• Image Registration is widely used in remote sensing, medical imaging, computer vision and many other applications.

• In general, its applications can be divided into three main groups like:• different times• different viewpoints• different sensors


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Presentation Outline

• Objective and Contribution.• Literature Survey.• Registration Techniques.• Spatial Transformation.• Database Description.• Methodology.• Experiment Result.• Conclusion and Future Work.• Acknowledgment.• References.


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Objective and Contribution

• This work is an attempt to performed the image registration task which is based on Affine transformation and justify the approach by testing over a newly created North-East Indian (NEI) face database.

• Our contribution in this paper is that, we have reported the registration task on our own created database which comprises facial images of numerous individuals and includes essential metadata, such as age, sex, tribe, non tribe based on affine transformation, which performs different rotation, translation and scaling selecting different control points of two same scenes.First International Conference on Intelligent Infrastructure, 1-2 December, 2012

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Literature Survey

• K.V. Arya et al. [1] presented a technique for robust image registration based on M-estimation Correlation Coefficient (MCC). A real valued correlation mask function is computed using Huber and Tukey’s robust statistics and is used as a similarity measure for registering image windows.

• J. Sarvaiya et al. [2] presented an improved feature point selection and matching technique for image registration. This technique is based on the ability of Non subsampled Contourlet Transform (NSTC) to extract significant features irrespective of feature orientation.


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Continue..

• Li et al. [3] presented a contour-based approach to register images from multiple sensors. The success of their method depends on the assumption that the common structures of images must be preserved well. Therefore, their method is efficient but works well only on cases where the contour information is well preserved.

• De Castro and Morandi [5] presented an elegant method, called phase correlation, to overcome this problem.


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Continue..

• Zheng and Chellappa [4] presented a method for determining the rotation parameter. They used a Lambertian model to model an image. Under the assumption that the illumination source is stationary, they use a shape-from-shading technique to estimate the illuminant directions of images.

• By adopting the method presented by Manjunath et al. [6], a number of feature points are extracted from the image pair. Then, these feature points are matched by using an area-based method in a hierarchical image structure.


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Registration Techniques

• Fundamental steps of Image Registration:• Step1: Read the images;• Step2: Choose control points in the images;• Step3: Save the control point pairs;• Step4: Specify the type of transformation and

infer its parameters;• Step5: Transform the unregistered image;


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Spatial Transformations

• Rigid• Perspective• Projective• Spline• Affine


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Affine Transformations

• Rotation• Translation• Scale• Shear• Stretch

Fig. 1 Elementary geometric transforms for a planar surface element used in the affine transform: translation, rotation, scaling, stretching, and shearing.


Database Description

• The North-East Indian (NEI) Face Database, which still under development and being develop by Biometric Laboratory of Tripura University of India [7].

• The NEI Face Database will contain the face images collected from the 7 north-eastern states of India, namely:– Arunachal Pradesh, Assam, Manipur, Meghalaya,

Mizoram, Nagaland, and Tripura.


Database Description

• In this database, there are 4 types of illumination condition:• Full Illumination, Half Illumination, Right Light On,

and Left Light On• Total eight different types of expressions:

• Neutral, Anger, Laughter, Sad, Surprised, Fear, Disgust and Closed Eye

• Five Different Poses:• +50°, +25°, 0°, -25°, -50°


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Methodology

• In this paper, we have considered all the images from full illumination as a base image and their corresponding half illumination images as unregistered image respectively.

• The face images are taken from North-East Indian (NEI) Face Database and register them with corresponding to their pose images under full illumination condition using ‘affine transformation’.

• The registration process is illustrated below:


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Methodology

• In the first step, two images are loaded as base and unregistered image respectively.

Fig. 2 Sample images of Base image and Unregistered image of North-East Indian (NEI) face database.


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Methodology

• After load both the images, we have to select the control points from the images as a pair. The selection process must be followed in an order from base image to unregistered image.

Fig. 3 Control point selection process


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Methodology

• As we mentioned earlier, here we have used the affine transformation type for registering the image. For affine transformation type, at least three control point pair must be selected.

• After saving the control point pairs, we use affine transformation to register the image.

Fig. 4 Registered image according to the Base image


Experiment Results

• Total fourteen thousand six hundred images of two hundred ninety two people of three different states have been registered.

• Some details of the database along with the number of registered and unregistered images have been shown in Table 1.


Experiment Results

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Name of State

No. of Subjects

Total No. of Images

No. of Base Image/Subject

No. of Unregistered

Image/Subject

Time for Registration

Mizoram 112 10640

40 503 min/ mage

Assam 107 10165

Tripura 73 6935

Table 1: Details of Registered Data


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Experiment Results

Fig. 5 (a) Different Base images of a person in full illumination, (b) Different Unregistered images of a person in half illumination, right light on and left light on, (c) Corresponding registered images of (b)


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Experiment Results

Fig. 6 (a) Different base images of a person in full illumination, (b) Different Unregistered images of a person in half illumination, (c) Corresponding registered images of (b)


Conclusion and Future Work

• In this paper, we have performed the image registration task which is based on Affine transformation and justify the approach by testing over a newly created North-East Indian (NEI) face database.

• In future, we will release the NEI face database, which will contain the benchmark face images collected from different north-eastern states of India, the accuracy of the database will be tested by baseline algorithms; and we will also develop an automatic image registration technique based face landmark detection.


Acknowledgment

• The research has been supported by the grant from DeitY, MCIT, Govt. of India, Vide No. 12(2)/2011-ESD, dated 29/03/2011.


References1. K.V. Arya a, P. Gupta, P.K. Kalra, P. Mitra, “Image

registration using robust M-estimators”, Pattern Recognition Letters, Vol. 28, pp. 1957–1968, 2007.

2. J. Sarvaiya, S. Patnaik, H. Goklani, “Image Registration using NSCT and Invariant Moment”, International Journal of Image Processing (IJIP), vol. 4: issue 2.

3. H. Li, B. S. Manjunath, and S. K. Mitra, “A contour-based approach to multisensor image registration”, IEEE Trans. Image Processing, vol. 4, no. 3, pp. 320–334, March 1995.

4. Q. Zheng and R. Chellappa, “A computational vision approaches to image registration”, IEEE Trans. Image Process, vol. 2, no. 3, pp. 311–326, 1993.


Continue..5. E. De Castro and C. Morandi, “Registration of translated

and rotated image using finite Fourier transform”, IEEE Trans. Pattern Anal. Machine Intell., vol. 9, no. 5, pp. 700–703, Sept. 1987.

6. B. S. Manjunath, R. Chellappa, and C. Malsburg, “A feature based approach to face recognition”, in Proceedings, IEEE Conference on Computer Vision Pattern Recognition, 1992, pp. 373–378.

7. K. Saha, R. Debnath, M. K. Bhowmik, D. Bhattacharjee, and M. Nasipuri, “North-East Indian Face Database: Its Design and Aspects”, Proceedings of the 4th International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom 2012), Bangalore, India, published by Springer, LNEE, pp. 450 – 456, Oct. 19-20, 2012.


Thank You


WELCOME 1 First International Conference on Intelligent Infrastructure, 1-2 December, 2012.

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