Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition

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1 Retna Swami MSSK, Karuppiah M. Optimal feature extraction using greedy approach for random image components and subspace approach in face recognition. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 28(2): Mar DOI /s Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition Mathu Soothana S. Kumar Retna Swami 1 and Muneeswaran Karuppiah 2 1 Department of Information Technology, Noorul Islam University, Thuckalay , India 2 Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi , India rmsskdhujaa@gmail.com; kmuni@mepcoeng.ac.in Received January 22, 2012; revised January 11, Abstract An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis (PCA) and multiple discriminant analysis (MDA) is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection (ORICS) plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature. Keywords face recognition, multiple discriminant analysis, optimal random image component selection, principal component analysis, recognition accuracy 1 Introduction In today s technology innovation era, where computers are an essential nutrient to fulfill and serve all the events, the need for secured, reliable, modest and flexible system has advertently become a challenging concern for the organizations. The technology encroachment has been a boon for speedy attainments of activity goals, but at the same time the security openings and transaction fakes are on the rise. Thus, the biometric technology [1] has taken its step to avoid any security openings and fakes. As one of the utmost successful applications of image analysis and understanding, face recognition has recently attracted significant attention, especially during the past several years. Even though current machine recognition systems have touched a certain level of maturity, their attainment is limited by the conditions enforced by many real applications. For example, recognition of face images acquired in an outdoor environment with variations in illumination and/or pose remains a largely unanswered problem. In recent years, local matching methods [2-5] have shown reasonable fallout not only in face recognition but also in other visual recognition tasks. The general view of local matching methods is to first trace several facial features (components), and then categorize the faces by comparing and merging the corresponding local statistics. Heisele et al. compared local component and global (holistic) approaches and observed that the component system outdid the global systems for recognition rates by larger than 60% [6-7]. Due to increasing importance, stand-alone segments were explicitly dedicated to local matching approaches in current investigations. We believe that a related comparative study on the choices at each step in the local matching process will benefit face recognition through localized matching. This paper offers a general outline for the local matching approach by analyzing, comparing and extending the recent methods for face recognition through localized matching. Gabor wavelets [8-9] only emphasize on some local areas in the face and extract information of precise scale and orientation within these local areas with different scales and orientations. Evidently, Gabor wavelets with a certain orientation yield edges and bars along the direction, and Gabor wavelets with a certain scale extract facts in the corresponding frequency band. Therefore, Short Paper 2013 Springer Science + Business Media, LLC & Science Press, China

2 Mathu Soothana S. Kumar Retna Swami et al.: ORICS Subspace in Face Recognition 323 Gabor wavelets extract added facts in some significant facial areas such as eyes, nose, mouth and even skin areas, which are very useful for face representation. In addition, the image component based representation [10] is more robust to illumination disparity than the holistic representation. The reason is that the illumination disparity within each image component is much smaller than that of holistic face image. While grouping Gabor features, some problems arise and they can be overcome by accurate localization of facial features which is still very challenging. Here, we propose a random image component selection (RICS) algorithm using greedy approach that automatically determines the positions and sizes of the local image components. The optimal random image component selection (ORICS) algorithm is applied to a local random image component, to check optimality and to decide whether the local image component is optimal or not, and thereby it gives high discriminability from a large number of possible local random image components. The rest of the paper is organized as follows. Some of existing methods such as conservative PCA, MDA and Gabor wavelets are outlined in Section 2. The importance of the proposed optimal random image component selection via greedy approach is described in Section 3. Results and discussions are highlighted in Section 4 followed by conclusions in Section 5. 2 Related Work The subspace approaches principal component analysis (PCA), multiple discriminant analysis (MDA) for dimensionality reduction and Gabor wavelets for extracting appropriate features based on orientations and frequency are addressed in the following subsections. 2.1 Principal Component Analysis PCA [11-13] is a popular geometric approach for facial image identification, in which, face images are articulated as a subset of their eigenvectors called eigenfaces. It is a technique that is used for dimensionality reduction in computer vision predominantly in facial recognition. A set of images corresponds to, a set of points in a high-dimensional space. Since, facial images are similar in composition, these points will not be randomly distributed and therefore can be described by a lower dimensional subspace. PCA assigns the basis vector for this lower dimensional space called face space, also called eigenvector, which may be calculated from covariance matrix of the original facial images. The image matrix Img of size (Mx My) is converted to the image vector I of size (N 1) where N = (Mx My); that is the image matrix is reconstructed by adding each column one after the other. Let I 1, I 2,..., I M be the training set of face images. The average face of the training image vectors at each pixel point with size (N 1) is defined by: A = 1 M M I i. (1) i=1 The mean subtracted image (Y ) is the difference of the training image from the mean image obtained by: Y = I A. (2) The difference matrix (D) is the matrix of all the mean subtracted training image vectors shown in: D = (Y 1 Y 2 Y 3 Y M ). (3) The covariance matrix (C) of the training image vectors is obtained by: C = D D T = 1 M M Y i Y T i. (4) i=1 The eigenvectors of the covariance matrix are computed and the E significant eigenvectors are chosen as those with the largest corresponding eigenvalues. Next, the training images are projected into the eigenface space and the weight (W k ) of each eigenvector to represent the image in the eigenface space is computed by: W k = E T k (I i A), i, k, (5) where E k represents the eigenvectors corresponding to the E largest eigenvalues of C and k varies from 1 to E. When a new test image is to be classified, it is also mean subtracted and projected onto the eigenface space and the classification of the test image vector in the standard eigenface method can be done by calculating the Euclidean distance of the test image vector to the mean of each class of the training image vectors. PCA encodes the pattern information based on second order dependencies, i.e., pixel wise covariance among the pixels, and are insensate to the dependencies of multiple (more than two) pixels in the patterns. The principal components are uncorrelated because the eigenvalues that may be calculated in PCA [11] should satisfy ortho-normal property. 2.2 Multiple Discriminant Analysis The preceding algorithm takes advantage of the fact that, under admittedly idealistic conditions, the disparity within classes lies in a linear subspace of the image space. Hence, the classes are convex, and therefore linearly distinguishable. Dimensionality reduction by linear projection can be accomplished and also preserves

3 324 J. Comput. Sci. & Technol., Mar. 2013, Vol.28, No.2 linear separability in face recognition problem but insensitivity to lighting conditions. Since the learning set is labeled, it makes sense to use this information to build a more consistent method for sinking the dimensionality of the feature space. So, by using class-specific linear methods for dimensionality reduction and simple classifiers in the reduced feature space, one may get enhanced recognition rates than with either the linear subspace method or the eigenface method. MDA [14-15] is an example of class specific method, where it attempts to shape the scatter in order to make it more consistent for classification, and the ratio of the between-class scatter and the within class scatter ratio are maximized. Let the between-class scatter matrix be defined by: S B = c N i (µ i µ)(µ i µ) T (6) i=1 and the within-class scatter matrix defined by: S W = c i=1 x k class i (x k µ i )(x k µ i ) T, (7) where µ i be the mean image of class X i, µ be the total mean of all samples and N i is the number of samples in class X i. If S W is nonsingular, the optimal projection W opt is chosen as the matrix with orthonormal columns which maximizes the ratio of the determinant of the between-class scatter matrix of the projected samples to the determinant of the within-class scatter matrix of the projected samples, i.e., W T S B W W opt = arg max W W T S W W = (w 1 w 2 w 3 w m ), where {w i i = 1, 2,..., m} is the set of generalized eigenvectors of S B and S W corresponding to the m largest generalized eigenvalues {λ i i = 1, 2,..., m} defined by: S B w i = λ i S W w i, i = 1, 2,..., m. (8) Note, there are utmost c 1 nonzero generalized eigenvalues, therefore upper bound on m is c 1, where c is the number of classes. 2.3 Gabor Feature Extraction The physical characteristics of the Gabor wavelets (filters) [16], specifically for frequency and orientation representations, are analogous to those of the human visual system, and they have been found to be predominantly appropriate for texture representation and discrimination. The two-dimensional (2-D) Gabor wavelet may be obtained by ϕ π(f,θ,γ,η) (x, y) = f 2 x = x cos θ + y sin θ, πγη exp[ (α2 x 2 + β 2 y 2 )] exp(j2πfx ), y = x sin θ + y cos θ, (9) where f is the central frequency of the sinusoidal plane wave, θ is the anti-clockwise rotation of the Gaussian and the plane wave, α is the sharpness of the Gaussian along the major axis parallel to the wave, β is the sharpness of the Gaussian minor axis perpendicular to the wave, γ = f α and η = f β are defined to keep the ratio between the frequency and sharpness constants. In Gabor wavelet, the filters are self-similar, that is in the shape of plane waves with frequency f, restricted by a Gaussian envelope function with relative width α and β is generated from one mother wavelet by dilation and rotation. ϕ u,v = ϕ π(f,θ,γ,η), f u = f max 2 u, θ v = v 8 π, u = 0, 1,..., U 1, v = 0, 1,..., V 1. (10) In (10), f max represents the highest peak frequency, U and V represent the number of scales and orientations respectively. 3 Proposed Framework for Local Image Component In this section, we emphasize the creation of local facial components that reflect and encode more detailed variations for finer representation. Local facial variation caused by expression, pose, illumination, occlusion, etc. may be dealt with more proficiently by providing a component-based feature extraction approach. This can enhance the categorization capability. Fig.1 depicts the model of local facial components by random image components. The local image components are generated according to the facial features such as eyes, nose, mouth and skin areas. However, this requires accurate localization of facial features which is still very challenging. In the aforementioned work, the image components are exaggeratedly and empirically designed. Here, an image component selection method determines the position and size of the local random image components. In our work, whenever the random image component is created for the first time, the optimal image component selection algorithm is applied to

4 Mathu Soothana S. Kumar Retna Swami et al.: ORICS Subspace in Face Recognition 325 Fig.1. Illustration of the concept of local image component creation and dimensionality reduction of the feature vector. the local random image component, to check optimality and to decide whether the local image component is optimal or not. Thus it gives high discriminability from a large number of possible local random image components. Likewise, the process is repeated for each and every image component of the first image to find out optimal image components and this can be extended to all the remaining patterns of the same class. 3.1 Optimal Image Component Selection The face imagery is normalized to a size in our work to reduce the processing speed. In our approach, for each and every image, several random image components of size ranging from [16, 64] [16, 64] are generated by using greedy search algorithm as depicted in Fig.2. The optimal image component selection algorithm is applied to check whether the selected local random image component is optimal or not. In optimal image component selection algorithm, initially an empty set is created. Then, a newly generated image component is compared with the already created image component. If the latter resembles the former within a threshold limit, then it can be excluded. Otherwise, the newly generated image component can be added to the set. Likewise, the process is repeated for the first image, and this can be extended for all the images in the dataset. After optimal image component selection, Gabor feature vector is extracted from each optimal image component. Here, 12 Gabor wavelets (3 scales and 4 orientations) are used which are highlighted in Fig.3. Since the image component size ranges between and 64 64, the maximum dimensionality is (64, 64, 3, 4), which is too high dimensional. To solve this problem, the Gabor features are uniformly down-sampled to 8 8 grid and are averaged on all the orientations and scales. The down-sampled Gabor Fig.2. Creation of optimal random local image components of Fig.3. Real part of the 2-D Gabor wavelets with scale and orien- size [16, 64] [16, 64] using greedy approach. tations.

5 326 J. Comput. Sci. & Technol., Mar. 2013, Vol.28, No.2 features are then projected onto the respective Eigen and Fisher subspaces for dimension reduction. A similar method of image component creation is applied for the query image, and each image component is projected onto the respective Eigen and Fisher subspaces where the classification can be taken based on the minimum distance measure. The overall process is outlined in the following algorithm: Algorithm 1. searchoptimalcomponent() //Input: Given Image I //Output: Optimal number of image components (OICs) { OICs = { } I 1 = fillzeros(i) //I 1: Image I fill with zeros CIC = selectrandomic (I) //CIC: CurrentImage Component OICs = OICs CIC // : union operation While (TRUE) { for each IC in OICs { if (!issimilar(ic, CIC)) { OICs = OICs CIC I 1 = placeat(i 1, CIC ) } Cnt = countmatchpixels(i, I 1) if (!Cnt < threshold) break } CIC = selectrandomic(i) } } 4 Results and Discussions The performance of ORICS, ORICS + PCA and ORICS + MDA was evaluated with three image databases, FERET, ORL and YALE. The FERET database consists of images with varying pose and expression; the ORL database consists of images with varying expression, lighting and details; and the YALE database consists of images with varying illumination and expression. Table 1 shows the details of the experimental image databases. We split the total images into training and testing which are disjoint. The number of image components Table 1. Experimental Image Databases Database Number of Number of Samples Image Classes in Each Class Size FERET (fafb) ORL YALE generated is By means of optimality random image component selection method, optimal image components are selected and by means of Gabor wavelets, local features are extracted and are downsampled to 8 8 grids and the recognition accuracies are noted. 4.1 ORICS onto Subspaces for Varying Image Size In LEC (Local Ensemble Classifier) method used in [10], after the local image components are selected, Gabor features are extracted from each local image component using Gabor filters. The down-sampled features of each feature vector are then further used to train a local image component. Finally, local image components are combined to form the LEC whose highest recognition accuracy along with the proposed approach ORICS, ORICS + PCA and ORICS + MDA for 90% training for varying size of an image is shown in Table 2. From the performance of these algorithms, it is noticed that the proposed method ORICS with subspace approach of MDA yields better recognition accuracy when varying the size of an image as shown in Fig.4. The proposed method has shown better performance than Exis.LEC without subspace approach (ORICS) and ORICS with subspace approach of PCA. Also, we observe from the results that the proposed method ORICS with subspace approach of MDA outperforms all the other methods with significant improvements under the condition of varying pose, illumination, expression, partial occlusion, etc. Table 2. Comparison of the Highest Accuracy Achieved by Several Methods for Varying Image Size Database Approach Image Size FERET ORICS ORICS+PCA ORICS+MDA ORL ORICS ORICS+PCA ORICS+MDA YALE ORICS ORICS+PCA ORICS+MDA Existing [10] Local Features (LEC)

6 Mathu Soothana S. Kumar Retna Swami et al.: ORICS Subspace in Face Recognition 327 Fig.4. Comparison of the recognition accuracy for the ORICS method with/without subspace approach with existing local features (Exis. LEC) for varying size of an image (FERET). 4.2 ORICS onto Subspaces for Image Here, we conduct tests for pose variations, illumination variations and large expressions using the images in the FERET, ORL and YALE databases to calculate the recognition accuracy and false rejection rate (FRR). As before, we vary the size of an image to observe the effect it has on face recognition. In our work, we achieve better results for 30% training. Fig.5 shows the recognition accuracies of various subspace methods for FERET, ORL and YALE databases. ORICS + MDA performs the best among these algorithms. A recognition rate as high as 100% is achieved for the novel ORICS + MDA approach, based on the optimal image components. In GC (Global Classifier) method used in [10], the Fast Fourier Transform (FFT) algorithm is applied onto the holistic image of size , in which lowfrequency Fourier coefficients, which cover about 50% of all the energy to form the global features are taken into account. To make full use of the discriminative information in both the global and the local features for improving the system performance, GC and LEC are combined to form the hierarchical ensemble classifier (HEC) whose recognition accuracies on FERET are noted. We observe from the results that the ORICS + MDA method completely outperforms the existing HEC method, ORICS and ORICS + PCA method in all aspects, specifically for optimal local image components with varying size [16, 64] [16, 64] (see Table 3). Fig.6 shows the performance comparison of the existing and proposed methods of FERET database. Table 3. Performance Comparison of FERET Method Recognition Accuracy HEC [10] 99 ORICS 85 ORICS+PCA 90 ORICS+MDA 100 Note: The result of HEC is cited from [10]. 5 Conclusions This study highlights a face recognition technique using FERET, ORL and YALE face databases. The feature selection strategy is robust to pose, illumination, expression and partial occlusions in the face images. By Fig.5. Comparison of the recognition accuracy for the ORICS method with/without subspace approach. (a) FERET database. (b) ORL database. (c) YALE database.

7 328 J. Comput. Sci. & Technol., Mar. 2013, Vol.28, No.2 Fig.6. Performance comparison of existing and proposed methods of FERET. using greedy approach, random image components for eyes, nose, mouth, skin areas, etc. are generated and by optimality checking, limited random image components are taken into account for every pattern. For large variations in pose, illumination, expression and partial occlusions, the proposed method ORICS using subspace approach with MDA completely outperforms LEC, ORICS, and ORICS using subspace approach with PCA method. The number of local image components was varied from 1 40 and by means of optimality checking, it was observed that at least image components are sufficient for reasonable recognition. The recognition accuracy is well at using 30% training set and improves very well at 50% training set as against the existing method. But all this occurred at the cost of surplus overhead of processing each local image component. However, nowadays computational assets could be exploited for fast response. Our future effort is to extend this work by combining local and global features and better feature extraction methods to further improve the recognition accuracy for 30% training. References [1] Zhao W, Chellappa R, Phillips P J, Rosenfeld A. Face recognition: A literature survey. ACM Computing Surveys, 2003, 35(4): [2] Gottumukkal R, Asari V. An improved face recognition technique based on modular PCA approach. Pattern Recognition Letters, 2004, 25(4): [3] Zou J, Ji Q, Nagy G. A comparative study of local matching approach for face recognition. IEEE Transactions on Image Processing, 2007, 16(10): [4] Retna Swami M S S K, Karuppiah M. An improved face recognition technique based on modular LPCA approach. Journal of Computer Science, 2011, 7(12): [5] Pentland A, Moghaddam B, Starner T. View-based and modular eigenspaces for face recognition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 1994, pp [6] Heisele B, Ho P, Wu J, Poggio T. Face recognition: Component-based versus global approaches. Computer Vision and Image Understanding, 2003, 91(1): [7] Fang Y, Tan T, Wang Y. Fusion of global and local features for face verification. In Proc. the 16th IEEE Int. Conf. Pattern Recognition, August 2002, Vol.2, pp [8] Lei Z, Liao S, Pietikäinen M, Li S. Face recognition by exploring information jointly in space, scale and orientation. IEEE Transactions on Image Processing, 2011, 20(1): [9] Liu C, Wechsler H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Processing, 2002, 11(4): [10] Su Y, Shan S, Chen X, Gao W. Hierarchical ensemble of global and local classifiers for face recognition. IEEE Transactions on Image Processing, 2009, 18(8): [11] Turk M, Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991, 3(1): [12] Turk M, Pentland A. Face recognition using eigenfaces. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 1991, pp [13] Sirovitch L, Kirby M. Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America, 1987, 4(3): [14] Xiang C, Fan X, Lee T. Face recognition using recursive fisher linear discriminant. IEEE Transactions on Image Processing, 2006, 15(8): [15] Belhumeur P, Hespanha J, Kriegman D. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): [16] Shen L, Bai L, Fairhurst M. Gabor wavelets and general discriminant analysis for face identification and verification. Image Vision and Computing, 2007, 25(5): Mathu Soothana S. Kumar Retna Swami got his B.E. degree in electronics and communication engineering in 1994 and M.E. degree in computer science and engineering in He is currently conducting his research on image processing in Anna University, Chennai. He is also an associate professor in the Department of Information Technology, Noorul Islam University, India. His research interest includes image processing, information coding, database management systems, data mining and multimedia. Muneeswaran Karuppiah got his B.E. degree in electronics and communication engineering from Thiagarajar College of Engineering, Madurai, in 1984, M.E. degree in computer science and engineering from PSG. College of Technology, Coimbatore in 1990, and Ph.D. degree in computer science and engineering from Manonmaniam Sundaranar University, India, Currently he is the professor and head of the Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, India. His area of interest includes image analysis, compilers, computer networks, intelligent system, security, grid and cloud computing.