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Proceedings of the International Conference , “Computational Systems and Communication Technology” 8th , MAY 2010 - by Cape Institute of Technology, Tirunelveli Dt-Tamil Nadu,PIN-627 114,INDIA FACIAL VERIFICATION USING A MODULAR KERNEL EIGEN SPACES 1 Golda Jude P, 2Dr. K. Muneeswaran 1 PG student 2Professor & Head Department of Computer science and Engineering Mepco schlenk engineering college Sivakasi, virudhunagar-626005 goldjude@gmail.com Abstract- In this paper face recognition accuracy is improved by developing a new kernel Eigen space and implemented on the phase congruency image extracted from the visual image. Smaller sub-regions in a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The proposed method helps to overcome the illumination, expression variations. ORL database is used for the experiment. In order to capture the relationships among more than two pixels, the data is projected into nonlinear higher dimensional spaces using the kernel method. The paper is organized as follows. A step to obtain the phase congruency image is explained in section 2. Modularization is explained in section 3. The process of obtaining modular kernel Eigen features is explained in Section 4. Architectural design is explained in section 5. Experimental result in section 6 followed by conclusion in 6. 2. PHASE CONGRUENCY Keywords-kernel,phasecongruency,Feature extraction 1. INTRODUCTION Face Recognition is a biometric identification by scanning a person's face and matching it against a library of known faces. A healthy human can detect a face easily and identify that face, whereas for a computer to recognize faces, the face area should be detected and recognition comes next. Hence for a computer to recognize faces the photographs should be taken in a controlled environment. The prime application of face recognition is to identify the individuals for the purpose of security. Even though face recognition technology [2] has moved from linear subspace methods [2] such as Eigen and Fisher faces [4], [5] to nonlinear methods namely kernel principal component analysis (KPCA) and kernel Fischer discriminate analysis (KFDA) [6]–[7], many of the problems are yet to be addressed. The feature selection process presented in this paper is derived from the concept of modular spaces [10]. The face images that affected due to variations such as non uniform illumination, expressions and partial occlusions are confined mostly to local regions. Modularizing the images would help to localize these variations, provided the modules created are sufficiently small. Linear subspace approaches such as PCA does not capture the relationship among more than two variables. Phase congruency is a method of edge detection that is particularly robust against changes in illumination and contrast. The concept is similar to coherence except that it applies to functions of different wavelength. For example, the Fourier decomposition of a square wave consists of sine functions, whose frequencies are odd multiples of the fundamental frequency. At the rising edges of the square wave, each sinusoidal component has a rising phase; the phases have maximal congruency at the edges. This corresponds to the human-perceived edges in an image where there are sharp changes between light and dark. A . Steps for calculating phase congruency image Convolve the face image I(x,y) with a bank of 2-D log Gabor filter with different orientations and scales. The 2-D log Gabor is constructed using a Gaussian function in the angular direction which is given by G( ) e( ( 0 ) 2 ) /( 2 02 ) (1) 6 orientations and 3 scales are chosen. 0 is the orientation of the filter and is the standard deviation of the Gaussian function. The log Gabor has a transfer function of the form G(w) e ( log(w / wo ) 2 ) /(2(log(k / wo ) 2 )) (2) Proceedings of the International Conference , “Computational Systems and Communication Technology” 8th , MAY 2010 - by Cape Institute of Technology, Tirunelveli Dt-Tamil Nadu,PIN-627 114,INDIA The amplitude of the response at a given scale and orientation is computed by Ano eno ( x, y) 2 ono ( x, y) 2 (3) The phase congruency of the image calculated over various scales and orientation is calculated by PC ( x, y ) o ( n eno ( x, y )) 2 ( n ono ( x, y )) 2 (4) o n Ano ( x, y ) Where represents the even and odd components at a scale n and orientation o Fig. 1. Original image fig. 2. Phase congruency image obtained from the original image 3. MODULARIZATION Several experimental results show that with module sizes of 4 X4, 8X 8, and 16X 16 on face images of size 64 X64. It has been observed in [9] that, maximum recognition accuracy is obtained when the images were divided into sub-regions of size 8X8.Larger module size (16X16) leads to lesser localization of the facial variations and smaller module size (4 X4) misses the sub-region information content. This leads to the conclusion that the sub-regions in the modular approach needs to occupy specific facial feature information. Hence, in the proposed neighborhood defined modular space approach, several 8X8 modules are created by combining 4X 8 modules in a neighborhood region of size 16X 16 for a 64X64 face image. As a result 448 modules are created[1] and then each module is projected to the Eigen spaces and then it is classified according to the minimum distance measure. A.General steps for proposed modularization for NXN image size 1. Each image is divided into ( N / n) ( N / n) to get n n to get number of large modules. 2. Each module of size ( N / n) ( N / n) is then divided into modules of size ( N / n i ) ( N / n j ) , where i j is the number of small modules within a modules. 3 .P modules can then be created by merging i j number of small modules in a neighborhood according to the relation P ((i j )!) / k!(i j k )!) , where k is the number of small modules to merge. 4. OBTAINING MODULAR KERNEL EIGEN FEATURES Kernel PCA has been applied to face recognition applications and is observed to be able to extract nonlinear features. The process of obtaining the weights for the input patterns in the kernel principal component analysis transformed space is described below. Let x i be the vectors belonging to the training sample set X {xi , x2 , x3 ....xn } where x i R n and ‘c’ be the number of classes in the training set. Mean of the data mo is given by mo 1 m xk 0 m k 1 (5) Let Φ be the mapping between the input space X and the feature space Φ: X->H, H can be assumed to be a Hilbert space Covariance matrix C is calculated as C V V (6) The relationship between the eigenvector and the sample training vector in feature space is m V i ( xi ) i 1 The projection of the sample vector vector is given by (7) onto the Eigen Proceedings of the International Conference , “Computational Systems and Communication Technology” 8th , MAY 2010 - by Cape Institute of Technology, Tirunelveli Dt-Tamil Nadu,PIN-627 114,INDIA V . ( xk ) m i 1 i k ( xi ,xk ) Voting (8) 5. ARCHITECTURAL DESIGN Result A. Steps in training image 1. Obtain the phase congruency maps for each image of the training set . 2. Modularize each of the training images. 3. Create and process each set of modules separately. 4. Generate the kernel matrix for each vectorized module set after an appropriate kernel is selected. 5. Apply KPCA for each module set and obtain the weights for all the individual modules. B. steps in test image 1. Extract the phase congruency features of the test image. 2. Create the modular regions. 3. Obtain the weights for each individual module using the vectorized modules and the Kernel matrix 4. Classify each module by using a minimum distance classifier on the generated weights from the training and the testing phase. Fig.3. Face recognition technique for training image 6.RESULTS For experiment purpose ORL database is used. There are 40 individuals. There are 10 images per individual in the ORL database with 40 images. Different tests are conducted, such as changing the training and test images used for each individual and using less or more number of training and test images in order to test the systems reaction to these changes. A .Neighborhood Defined Modular phase congruency Based Kernel PCA (NMPKPCA) The modules created are projected to higher dimensional spaces using kernel spaces. Table 1 shows the recognition rate obtained for the different test and train images. The train and test images are disjoint. Table 1 shows 80,160,180 train images with 2,4,6 images per individual are trained respectively. ORL database is used for the experiment to test and train the different images. Similarly 40,80,120 test images with 1,2,3 images per individual are tested respectively. TABLE 1 Phase congruency feature extraction RECOGNITION RATE FOR DIFFERENT TEST AND TRAIN IMAGES Extraction of modules and projection into Eigen subspaces Nearest neighbor classification of modules No of test images No of train images Recognized image Not Recognized image Recognition Rate in % 40 80 35 5 87.5 80 160 75 5 93.6 120 180 112 8 95 Proceedings of the International Conference , “Computational Systems and Communication Technology” 8th , MAY 2010 - by Cape Institute of Technology, Tirunelveli Dt-Tamil Nadu,PIN-627 114,INDIA 7. CONCLUSION AND FUTURE WORK This paper presents a face recognition technique using visual face images. The feature selection is robust to the variations that occur in the face images captured in visual. The novel modular kernel Eigen spaces approach has been able to provide high recognition accuracy in images affected due to partial occlusions, expressions and nonlinear lighting variations. 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