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Learning to Recognize Faces by Local Feature Design and Selection

Authors: Yanwei Pang, Lei Zhang, Zhengkai Liu


Studies in neuroscience suggest that both global and local feature information are crucial for perception and recognition of faces. It is widely believed that local feature is less sensitive to variations caused by illumination, expression and illumination. In this paper, we target at designing and learning local features for face recognition. We designed three types of local features. They are semi-global feature, local patch feature and tangent shape feature. The designing of semi-global feature aims at taking advantage of global-like feature and meanwhile avoiding suppressing AdaBoost algorithm in boosting weak classifies established from small local patches. The designing of local patch feature targets at automatically selecting discriminative features, and is thus different with traditional ways, in which local patches are usually selected manually to cover the salient facial components. Also, shape feature is considered in this paper for frontal view face recognition. These features are selected and combined under the framework of boosting algorithm and cascade structure. The experimental results demonstrate that the proposed approach outperforms the standard eigenface method and Bayesian method. Moreover, the selected local features and observations in the experiments are enlightening to researches in local feature design in face recognition.

Keywords: Face Recognition, adaboost, local feature, subspace analysis

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[1] W. Zhao, R. Chellappa, A. Rosenfeld, J. Phillips, "Face recognition: A Literature Survey," ACM Computing Surveys, vol. 12, 399-458, 2003.
[2] B. Bruce, Recognizing Faces, London, Lawrence Erlbaum Associates, 1998.
[3] L. Sirovich, M. Kirby, "Low -dimensional Procedure for the Characterization of Human Face," Journal of the Optical Society of America, vol. 4, pp. 519-524, 1987.
[4] M. Turk, A. Pentland, "Face Recognition Using Eigenfaces," IEEE CVPR, 586-591, 1991.
[5] D.L Swets, J. Weng, "Using Discriminant Eigenfeatures for Image Retrieval," IEEE PAMI, vol. 18, pp. 831-836, 1996.
[6] N.B. Peter, P.H. Joao, J.K. David, "Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE PAMI, vol. 19, no. 7, 711-720, 1997.
[7] B. Moghaddam, B. Jebara, A. Pentland, "Bayesian Face Recognition," Pattern Recognition, vol. 33, 1771-1782, 2000.
[8] X. Wang, X. Tang, "Unified Subspace Analysis for Face Recognition," IEEE ICCV, pp. 679-686, 2003.
[9] R. Brunelli and T. Poggio, "Face Recognition: Features versus Templates", IEEE PAMI, vol. 15, no. 10, 1993, pp. 1042-1052.
[10] A. Pentland, B. Moghaddam, T. Starner, "View-Based and modular eigenspaces for face reocognition,", IEEE CVPR, pp. 84-91, 1994.
[11] F.S. Samaria, "Face Recognition Using Hidden Markov Models". PhD thesis, University of Cambridge, 1994.
[12] L. Wiskott, J. Fellous, N. Kruger, C. Malsburg, "Face Recognition by Elastic Bunch Graph Matching," IEEE PAMI, vol. 19, no. 7, pp. 775- 779, 1997.
[13] P.S. Penev, "Local Feature Analysis: a General Statistical Theory for Object Representation," Network: Computation in Neural Systems, vol. 7, pp. 477-500, 1996.
[14] B. Heisele, T. Serre, M. Pontil, T. Vetter, T. Poggio, "Categorization by Learning and Combining Object Parts", Advances in Neural Information Processing Systems, vol. 2, pp. 1239-1245, 2002.
[15] S. Lucey, T. Chen, "A GMM Parts Based Face Representation for Improved Verification Through Relevance adaptation," IEEE CVPR, vol. 2, 855-861, 2004.
[16] R.E. Schapire, "A Brief Introduction to Boosting," Proc. ICAI, pp. 1401- 1406, 1999.
[17] P. Viola, J. Michael, "Rapid Object Detection Using a Boosted Cascade of Simple Features," CVPR, pp. 511-518, 2001.
[18] J. Peng, D.R. Heisterkamp, H.K. Dai, "LDA/SVM Driven Nearest Neighbor Classification," IEEE Trans. on Neural Networks, vol. 14, no. 4, pp. 940-942, 2003.
[19] B. Guo, K.M. Lam, K.Ho Lin, "Human Face Recognition Based on Spatially Weighted Hausdorff Distance," Pattern Recognition Letters, vol 24, no. 1-3, pp. 99-507, 2003.
[20] A.M. Martinez, A.C. Kak, "PCA versus LDA," IEEE PAMI, vol. 23, no. 2, pp. 228-233, 2001.
[21] Z. Hong, "Algebraic Feature Extraction of Image For Recognition," Pattern Recognition, vol. 24, pp. 211-219, 1991.
[22] Y. Tian, T. Tan, Y. Wang, Y. Fang, "Do Singular Values Contain Adequate Information for Face Recognition?" , Pattern recognition, vol. 36, pp. 649-655, 2003.
[23] A. Lanitis, C.J. Taylor, T.F. Cootes, "Automatic Face Identification System Using Flexible Appearance Models," Image and Vision Computing, vol. 13, pp. 393-401, 1995.
[24] Y. Zhou, L. Gu, H.J. Zhang, "Bayesian Tangent Shape Model," IEEE CVPR, vol. 1, pp. 109-116, 2003.
[25] P.J. Phillips, H. Moon, S. Rivzi, P. Rauses, "The FERET Evaluation Methodology for Face Recognition Algorithm," IEEE PAMI, vol. 22, pp. 1090-1104, 2000.