人臉辨識是非常熱門的研究領域，其技術廣泛應用於資訊安全、警察執法、人機互動、機場安全和視訊監控系統等。但辨識的人臉會因為有光源變化、姿勢、臉部表情、老化和遮蔽等情況而產生辨識錯誤。
本論文提出利用整體和局部的人臉來判別特徵作各種情況的人臉辨識。整體和局部的人臉判別特徵主要包含兩個人臉辨識的判斷資訊。整體特徵是擷取整張人臉影像，並使用區域的線性判別分析方法。區域特徵是擷取人臉影像特徵，像是右眼、左眼、鼻子和嘴巴，並使用區域二位元模式方法，描述這些局部區域的人臉特性和變化，再使用區域的線性辨別分析方法。區域線性判別分析保留影像之間區域結構的資訊。在分類器部分有5個分類器的結果，區域分類器結合右眼、左眼、鼻子和嘴巴的分類器結果，結合分類器結合整體分類器和區域分類器的結果。
使用MATLAB 2011B軟體，撰寫我們提出來整體和局部特徵架構。由實驗結果，得到利用整體和局部判別特徵比傳統人臉辨識方法更有效的辨識能力。

Face recognition has been an active research area due to its wide range of application in information security, law enforcement, human-computer interaction, airport security, and video surveillance systems. Face recognition commits errors such as illumination conditions, pose, facial expression, aging, partial occlusions.
In the thesis, we propose global and local discriminative features for face recognition under various facial conditions. The global and local discriminative features consists of two mainly discriminant information for face recognition. The global feature extracted from the whole face image using local linear discrimination analysis. The local feature selects four facial features using local binary patterns and local linear discrimination analysis. Local linear discriminant analysis can preserve the information of the region structure. The local classifier combines classifier of four local regions. The combination classifier combines the global and local classifiers.
As shown in experimental result, the global and local discriminative features can have more effective discriminative power than traditional face recognition methods.

References

[1] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face recognition: A literature survey," Acm Computing Surveys (CSUR), vol. 35, pp. 399-458, 2003.
[2] T. Ahonen, A. Hadid, and M. Pietikainen, "Face description with local binary patterns: Application to face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 2037-2041, 2006.
[3] X. Tan and B. Triggs, "Enhanced local texture feature sets for face recognition under difficult lighting conditions," IEEE Transactions on Image Processing, vol. 19, pp. 1635-1650, 2010.
[4] J. H. Lai, P. C. Yuen, and G. C. Feng, "Face recognition using holistic Fourier invariant features," Pattern Recognition, vol. 34, pp. 95-109, 2001.
[5] W. Hwang, G. Park, J. Lee, and S. C. Kee, "Multiple face model of hybrid fourier feature for large face image set," 2006, pp. 1574-1581.
[6] Y. Su, S. Shan, X. Chen, and W. Gao, "Hierarchical ensemble of global and local classifiers for face recognition," IEEE Transactions on Image Processing, vol. 18, pp. 1885-1896, 2009.
[7] S. I. Choi and G. M. Jeong, "Shadow Compensation Using Fourier Analysis With Application to Face Recognition," IEEE Signal Processing Letters, vol. 18, pp. 23-26, 2011.
[8] M. Savvides, J. Heo, R. Abiantun, C. Xie, and B. V. K. V. Kumar, "Class dependent kernel discrete cosine transform features for enhanced holistic face recognition in FRGC-II," in IEEE International Conference on Acoustics, Speech and Signal Processing, 2006, pp. II-II.
[9] Z. M. Hafed and M. D. Levine, "Face recognition using the discrete cosine transform," International Journal of Computer Vision, vol. 43, pp. 167-188, 2001.
[10] W. Chen, M. J. Er, and S. Wu, "Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, , vol. 36, pp. 458-466, 2006.
[11] J. T. Chien and C. C. Wu, "Discriminant waveletfaces and nearest feature classifiers for face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 1644-1649, 2002.
[12] J. Ravi and S. S. Tevaramani, "Face Recognition using DT-CWT and LBP Features," 2012 International Conference on Computing, Communication and Applications (ICCCA), p. 6, 2012.
[13] J. G. Daugman, "Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters," Optical Society of America, Journal, A: Optics and Image Science, vol. 2, pp. 1160-1169, 1985.
[14] S. Liao, X. Zhu, Z. Lei, L. Zhang, and S. Li, "Learning multi-scale block local binary patterns for face recognition," Advances in Biometrics, pp. 828-837, 2007.
[15] L. Wolf, T. Hassner, and Y. Taigman, "Descriptor based methods in the wild," 2008.
[16] M. Heikkilä, M. Pietikäinen, and C. Schmid, "Description of interest regions with local binary patterns," Pattern Recognition, vol. 42, pp. 425-436, 2009.
[17] A. Hafiane, G. Seetharaman, and B. Zavidovique, "Median binary pattern for textures classification," Image Analysis and Recognition, pp. 387-398, 2007.
[18] F. Ahmed, E. Hossain, A. Bari, and A. Shihavuddin, "Compound local binary pattern (CLBP) for robust facial expression recognition," in IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI), 2011, pp. 391-395.
[19] W. Yang and C. Sun, "Face recognition using improved local texture patterns," in 2011 9th World Congress on Intelligent Control and Automation (WCICA), 2011, pp. 48-51.
[20] T. Jabid, M. H. Kabir, and O. Chae, "Local Directional Pattern (LDP)–A Robust Image Descriptor for Object Recognition," in 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), , 2010, pp. 482-487.
[21] W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, "Local Gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition," 2005, pp. 786-791 Vol. 1.
[22] S. Xie, S. Shan, X. Chen, and J. Chen, "Fusing local patterns of gabor magnitude and phase for face recognition," IEEE Transactions on Image Processing, vol. 19, pp. 1349-1361, 2010.
[23] M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of cognitive neuroscience, vol. 3, pp. 71-86, 1991.
[24] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 711-720, 1997.
[25] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, "Face recognition by independent component analysis," IEEE Transactions on Neural Networks, vol. 13, pp. 1450-1464, 2002.
[26] S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by locally linear embedding," Science, vol. 290, pp. 2323-2326, 2000.
[27] X. Niyogi, "Locality preserving projections," 2004, p. 153.
[28] X. He, S. Yan, Y. Hu, P. Niyogi, and H. J. Zhang, "Face recognition using laplacianfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 328-340, 2005.
[29] T. Zhang, B. Fang, Y. Y. Tang, Z. Shang, and B. Xu, "Generalized discriminant analysis: A matrix exponential approach," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 40, pp. 186-197, 2010.
[30] Q. Tian, M. Barbero, Z. H. Gu, and S. H. Lee, "Image classification by the Foley-Sammon transform," Optical Engineering, vol. 25, pp. 834-840, 1986.
[31] D. Q. Dai and P. C. Yuen, "Face recognition by regularized discriminant analysis," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,, vol. 37, pp. 1080-1085, 2007.
[32] H. Zhao and P. C. Yuen, "Incremental linear discriminant analysis for face recognition," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,, vol. 38, pp. 210-221, 2008.
[33] H. Xiong, M. Swamy, and M. Ahmad, "Two-dimensional FLD for face recognition," Pattern Recognition, vol. 38, pp. 1121-1124, 2005.
[34] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 971-987, 2002.
[35] M. A. Turk and A. P. Pentland, "Face recognition using eigenfaces," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1991, pp. 586-591.
[36] R. A. Fisher, "The use of multiple measurements in taxonomic problems," Annals of Human Genetics, vol. 7, pp. 179-188, 1936.
[37] C. Kim, J. Y. Oh, and C. H. Choi, "Combined subspace method using global and local features for face recognition," 2005, pp. 2030-2035 vol. 4.
[38] Y. Fang, T. Tan, and Y. Wang, "Fusion of global and local features for face verification," 2002, pp. 382-385 vol. 2.
[39] Y. Lee, K. Lee, and S. Pan, "Local and global feature extraction for face recognition," 2005, pp. 21-40.
[40] K. C. Lee, J. Ho, and D. J. Kriegman, "Acquiring linear subspaces for face recognition under variable lighting," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 684-698, 2005.