SVM-based Multiview Face Recognition by Generalization of Discriminant Analysis
Identity verification of authentic persons by their multiview faces is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. In the proposed work, the Gabor filter bank is used to extract facial features that characterized by spatial frequency, spatial locality and orientation. Gabor face representation captures substantial amount of variations of the face instances that often occurs due to illumination, pose and facial expression changes. Convolution of Gabor filter bank to face images of rotated profile views produce Gabor faces with high dimensional features vectors. Canonical covariate is then used to Gabor faces to reduce the high dimensional feature spaces into low dimensional subspaces. Finally, support vector machines are trained with canonical sub-spaces that contain reduced set of features and perform recognition task. The proposed system is evaluated with UMIST face database. The experiment results demonstrate the efficiency and robustness of the proposed system with high recognition rates.
[1] J. Y. Gan, and Y. W. Zhang, "A new approach for face recognition
based on singular value features and neural networks," Acta Electronica
Sinica, vol. 32, no.1, pp. 56 - 58, 2004.
[2] J. Y. Gan, Y. W. Zhang, and S. Y. Mao, "Adaptive principal components
extraction algorithm and its applications in the feature extraction of
human face," Acta Electronica Silica, vol. 30, no. 7, pp. 1013 - 1016,
2002.
[3] M. Dai, and M. Q. Zhou, "On automatic human face recognition,"
Advances Biometrics, vol. 1, pp. 41 - 48, 2003.
[4] M. Turk, and A. Pentland, "Eigenfaces for recognition", Journal of
Cognitive Neuroscience, vol. 3, no. 1, pp. 71 - 86, 1991.
[5] M. Turk, and A. Pentland, "Face recognition using eigenfaces",
Proceeding of the IEEE Conference on Computer Vision and Pattern
Recognition, 1991, pp. 586 - 591.
[6] P. Belhumeur, J. Hespanha, and D. Kriegman, "Eigenfaces vs.
fisherfaces: Recognition using class specific linear projection",
Proceeding of the Fourth European Conference on Computer Vision,
vol. 1, 1996, pp. 45 - 58.
[7] A. Martinez, and A. Kak, "PCA versus LDA", IEEE Transaction on
Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228 - 233,
2001.
[8] G. W. Cottrell, and M. K. Fleming, "Face recognition using
unsupervised feature extraction," Proceedings of the International
Conference on Neural Network, 1990, pp. 322 - 325.
[9] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, "Face recognition by
independent component analysis," IEEE Transaction on Neural
Networks, vol. 13, no. 6, pp. 1450 - 1464, 2002.
[10] M. H. Yang, "Kernel eigenfaces vs. kernel fisherfaces: Face recognition
using kernel methods," Proceedings of the Fifth IEEE International
Conference on Automatic Face and Gesture Recognition, 2002, pp. 215
- 220.
[11] C. J. C. Burges, "A tutorial on support vector machines for pattern
recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, pp.
121-167, 1998.
[12] J. G. Daugman, "Complete discrete 2-D gabor transforms by neural
networks for image analysis and compression", IEEE Transaction on
Acoustic, speech and signal processing, vol. 36, pp.1169 - 1179, 1998.
[13] T. S. Lee, "Image representation using 2D gabor wavelets", IEEE
Transaction on Pattern Analysis and Machine Intelligence, vol. 18,
pp.959 - 971, 1996.
[14] http://images.ee.umist.ac.uk/danny/database.html.
[15] H. Hotelling, "Relations between two sets of variates", Biometrika, vol.
28, pp. 321- 377, 1936.
[16] D. J. Beymer. "Face recognition under varying pose," MIT AI Lab,
Technical Report, 1993.
[17] A. Pentland, B. Moghaddam, and T. Starner, "View-based and modular
eigenspaces for face recognition", Proceedings of the International
Conference on Computer Vision and Pattern Recognition. 1994.
[18] V. Blanz, and T. Vetter, "A morphable model for the synthesis of 3D
faces," Proceedings of the International Conference SIGGRAPH, 1999,
pp. 187 - 194.
[19] J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "A kernel machine
based approach for multi-view face recognition," IEEE International
Conference on Image Processing, 2002, pp. 265 - 268.
[20] A. Rattani, D. R. Kisku, A. Logario, and M. Tistarelli, "Facial template
synthesis using SIFT features," IEEE Workshop on Automatic
Identification Advanced Technologies, 2007, pp. 69 - 73.
[1] J. Y. Gan, and Y. W. Zhang, "A new approach for face recognition
based on singular value features and neural networks," Acta Electronica
Sinica, vol. 32, no.1, pp. 56 - 58, 2004.
[2] J. Y. Gan, Y. W. Zhang, and S. Y. Mao, "Adaptive principal components
extraction algorithm and its applications in the feature extraction of
human face," Acta Electronica Silica, vol. 30, no. 7, pp. 1013 - 1016,
2002.
[3] M. Dai, and M. Q. Zhou, "On automatic human face recognition,"
Advances Biometrics, vol. 1, pp. 41 - 48, 2003.
[4] M. Turk, and A. Pentland, "Eigenfaces for recognition", Journal of
Cognitive Neuroscience, vol. 3, no. 1, pp. 71 - 86, 1991.
[5] M. Turk, and A. Pentland, "Face recognition using eigenfaces",
Proceeding of the IEEE Conference on Computer Vision and Pattern
Recognition, 1991, pp. 586 - 591.
[6] P. Belhumeur, J. Hespanha, and D. Kriegman, "Eigenfaces vs.
fisherfaces: Recognition using class specific linear projection",
Proceeding of the Fourth European Conference on Computer Vision,
vol. 1, 1996, pp. 45 - 58.
[7] A. Martinez, and A. Kak, "PCA versus LDA", IEEE Transaction on
Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228 - 233,
2001.
[8] G. W. Cottrell, and M. K. Fleming, "Face recognition using
unsupervised feature extraction," Proceedings of the International
Conference on Neural Network, 1990, pp. 322 - 325.
[9] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, "Face recognition by
independent component analysis," IEEE Transaction on Neural
Networks, vol. 13, no. 6, pp. 1450 - 1464, 2002.
[10] M. H. Yang, "Kernel eigenfaces vs. kernel fisherfaces: Face recognition
using kernel methods," Proceedings of the Fifth IEEE International
Conference on Automatic Face and Gesture Recognition, 2002, pp. 215
- 220.
[11] C. J. C. Burges, "A tutorial on support vector machines for pattern
recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, pp.
121-167, 1998.
[12] J. G. Daugman, "Complete discrete 2-D gabor transforms by neural
networks for image analysis and compression", IEEE Transaction on
Acoustic, speech and signal processing, vol. 36, pp.1169 - 1179, 1998.
[13] T. S. Lee, "Image representation using 2D gabor wavelets", IEEE
Transaction on Pattern Analysis and Machine Intelligence, vol. 18,
pp.959 - 971, 1996.
[14] http://images.ee.umist.ac.uk/danny/database.html.
[15] H. Hotelling, "Relations between two sets of variates", Biometrika, vol.
28, pp. 321- 377, 1936.
[16] D. J. Beymer. "Face recognition under varying pose," MIT AI Lab,
Technical Report, 1993.
[17] A. Pentland, B. Moghaddam, and T. Starner, "View-based and modular
eigenspaces for face recognition", Proceedings of the International
Conference on Computer Vision and Pattern Recognition. 1994.
[18] V. Blanz, and T. Vetter, "A morphable model for the synthesis of 3D
faces," Proceedings of the International Conference SIGGRAPH, 1999,
pp. 187 - 194.
[19] J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "A kernel machine
based approach for multi-view face recognition," IEEE International
Conference on Image Processing, 2002, pp. 265 - 268.
[20] A. Rattani, D. R. Kisku, A. Logario, and M. Tistarelli, "Facial template
synthesis using SIFT features," IEEE Workshop on Automatic
Identification Advanced Technologies, 2007, pp. 69 - 73.
@article{"International Journal of Information, Control and Computer Sciences:55745", author = "Dakshina Ranjan Kisku and Hunny Mehrotra and Jamuna Kanta Sing and Phalguni Gupta", title = "SVM-based Multiview Face Recognition by Generalization of Discriminant Analysis", abstract = "Identity verification of authentic persons by their multiview faces is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. In the proposed work, the Gabor filter bank is used to extract facial features that characterized by spatial frequency, spatial locality and orientation. Gabor face representation captures substantial amount of variations of the face instances that often occurs due to illumination, pose and facial expression changes. Convolution of Gabor filter bank to face images of rotated profile views produce Gabor faces with high dimensional features vectors. Canonical covariate is then used to Gabor faces to reduce the high dimensional feature spaces into low dimensional subspaces. Finally, support vector machines are trained with canonical sub-spaces that contain reduced set of features and perform recognition task. The proposed system is evaluated with UMIST face database. The experiment results demonstrate the efficiency and robustness of the proposed system with high recognition rates.
", keywords = "Biometrics, Multiview face Recognition, Gaborwavelets, LDA, SVM.", volume = "2", number = "8", pages = "2689-6", }
