本論文提出一個可以應用在相簿管理或數位相框的人臉偵測與人臉辨識方法，因為 一般生活照片的拍攝條件，都不太一樣，這會增加人臉偵測與人臉辨識的困難度。所以 在人臉偵測階段，採用膚色偵測法縮小搜尋人臉的範圍，以提升偵測效率。然後使用賈 伯小波抽取人臉整體特徵，再輸入倒傳遞類神經網路做訓練，將使得人臉偵測到的位置 與尺寸更為準確。
在人臉辨識階段，採用稀疏編碼(sparse coding)結合可導引濾波器(steerable filter)，
生活照片辨識率可達 80%，AR 資料庫辨識率為 94.4% ~ 98.0%，基本上，訓練樣本越多，
辨識率越高，但前提是，訓練樣本的定位點必須正確，否則，這些不準確的訓練樣本，
將成為辨識系統的干擾訊號，會增加誤判的機率。
本篇論文提出直方圖統計法來減少稀疏編碼的權重數目，目的是為了降低系統運算 量，同時特徵向量仍然具有代表性。
未來的展望，可以朝人臉辨識三個挑戰來探討，分別是姿態角度改變、明亮度不均 勻和表情變化這三大問題: 1.增加正確定位人臉位置的機會，而且精確地切割人臉形狀，
不讓特徵點偏移得太嚴重。2.正規化人臉像素的明亮度，並且允許一張明亮度不均勻的 訓練樣本而得到良好的人臉辨識率。3.建立 3D 立體稀疏編碼來允許人臉大角度或表情 的變化。
參 考 文 獻
[1] B. Moghaddam and A. Pentland, “Probabilistic Visual Learning for Object Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 696-710, 1997.
[2] H. A. Rowley, S. Baluja, and T. Kanade, “Neural Network-Based Face Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38, 1998.
[3] B. Heisele, T. Poggio, and M. Pontil, “Face Detection in Still Gray Images,” A.I. memo AIM-1687, Artificial Intelligence Laboratory, MIT, 2000.
[4] P. Viola and M. J. Jones, “Robust Real-Time Face Detection,” IJCV, vol. 57, pp. 137-154, 2004.
[5] P. Wang, M. B. Green, Q. Ji, and J. Wayman, “Automatic Eye Detection and Its Validation,” in Proc. CVPR, vol. 3, pp. 164-171, 2005.
[6] P. Kakumanu, S. Makrogiannis, and N. Bourbakis, “A Survey of Skin-Color Modeling and Detection Methods,” Pattern Recognition, vol. 40, pp. 1106-1122, 2007.
[7] S. Birchfield, “Elliptical Head Tracking Using Intensity Gradients and Color Histograms,”
Computer Vision and Pattern Recognition, pp. 232-237, 1998.
[8] L. Wiskott, J. M. Fellous, N. Kruger, and C. Malsburg, “Face Recognition by Elastic Bunch Graph Matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 775-779, 1997.
[9] T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active Shape Models - Their Training and Application,” Computer Vision and Image Understanding, pp. 38-59, 1995.
[10] T. F. Cootes, G. J. Edwards and C. J. Taylor, “Active Appearance Models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp 681-685, 2001.
[11] D. Maio and D. Maltoni, “Real-Time Face Location on Grayscale Static Images,”
Pattern Recognition, vol. 33, pp. 1525-1539, 2000.
[12] H. S. Lee and D. Kim, “Robust Face Tracking by Integration of Two Separate Trackers:
Skin Color and Facial Shape,” Pattern Recognition, vol 40, pp. 3225-3235, 2007.
[13] M. N. Francesc, A. Sanfeliu, and D. Samaras, “Integration of Deformable Contours and a Multiple Hypotheses Fisher Color Model for Robust Tracking in Varying Illuminate
Environments,” Image and Vision Computing, vol. 25, pp. 285-296, 2007.
[14] Y. Tong, Y. Wang, Z. Zhu, and Q. Ji, “Robust Facial Feature Tracking under Varying Face Pose and Facial Expression,” Pattern Recognition, vol. 40, pp. 3195-3208, 2007.
[15] J. Tu, H. Tao, and T. Huang, “Face as Mouse Through Visual Face Tracking,” Computer Vision and Image Understanding, vol.108, pp. 35-40, 2007.
[16] M. Kim, S. Kumar, V. Pavlovic, and H. Rowley, “Face Tracking and Recognition with Visual Constraints in Real-World Videos,” CVPR, pp. 1-8, 2008.
[17] W. Zheng and S. M. Bhandarkar, “Face Detection and Tracking Using a Boosted Adaptive Particle Filter,” Journal of Visual Communication and Image Representation, vol.
20, pp. 9-27, 2009.
[18] M. Balasubramanian, S. Palanivel, and V. Ramalingam, “Real Time Face and Mouth Recognition Using Radial Basis Function Neural Networks,” Expert Systems with Applications, vol. 36, pp. 6879-6888, 2009.
[19] Y. Zhang and A. M. Martinez, “A Weighted Probabilistic Approach to Face Recognition from Multiple Images and Video Sequence,” Image and Vision Computing, vo. 24, pp.
626-638, 2006.
[20] G. Shakhnarovich and B. Moghaddam, “Face Recognition in Subspaces,” Handbook of Face Recognition, Springer, 2004.
[21] A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman “From Few to Many:
Generative Models for Recognition Under Variable Pose and Illumination,” IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 23, pp. 643-660, 2001.
[22] A. R. Chowdhury, R. Chellappa, S. Krishnamurthy, and T. Vo, “3D Face Recostruction from Video Using a Generic Model,” ICME, vol. 1, pp. 449-452, 2002.
[23] D. Jiang, Y. Hu, S. Yan, L. Zhang, H. Zhang, and W. Gao, “Efficient 3D Reconstruction for Face Recognition,” Pattern Recognition, vol. 38, pp. 787-798, 2005.
[24] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” in IEEE PAMI, vol. 31, pp.210-227, 2009.
[25] M. Yang, L. Zhang, J. Yang, and D. Zhang, “Robust Sparse Coding for Face Recognition,” in CVPR, pp. 625-632, 2011.
[26] 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.
[27] S. Baker and T. Kanade, “Limits on Super-Resolution and How to Break Them,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 1167-1183, 2002.
[28] X. Liu, T. Chen and S. M. Thornton, “Eigenspace Updating for Non-Stationary Process and Its Application to Face Recognition,” Pattern Recognition, vo. 36, pp. 1945-1959, 2002.
[29] G. Shakhnarovich, J. W. Fisher, and T. Darrell, “Face Recognition from Long-Term Observations,” Computer Science, vol. 2352, pp. 851-865, 2002.
[30] W. Y. Zhao and R. Chellappa, “Symmetric Shape-from-Shading Using Self-ratio Image,” Computer Vision, vol. 45, pp. 55-75, 2001.
[31] Y. Li, S. Gong, and H. Liddell, “Constructing Facial Identity Surfaces in a Nonlinear Discriminating Space,” CVPR, vol. 2, pp. 258-263, 2003.
[32] N. Vaswani and R. Chellappa, “Principal Components Null Space Analysis for Image and Video Classification,” IEEE Transactions on Image Processing, vol. 15, pp. 1816-1830, 2006.
[33] S. Du and R. Ward, “Wavelet-Based Illumination Normalization for Face Recognition,”
ICIP, vol. 2, pp. 954-957, 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] O. Arandjelovic and R. Cipolla, “A Pose-Wise Linear Illumination Manifold Model for Face Recognition Using Video,” Computer Vision and Image Understanding, vol. 113, pp.
113-125, 2009.
[36] O. Arandjelovic, G. Shakhnarovich, J. Fisher, R. Cipolla, and T. Darrell, “Face Recognition with Image Sets Using Manifold Density Divergence,” CVPR, vol. 1, pp.
581-588, 2005.
[37] O. Arandjelovic and R. Cipolla, “An Illumination Invariant Face Recognition System for Access Control Using Video,” in Proceedings of the British Machine Vision Conference, pp.
537-546, 2004.
[38] V. Blanz and T. Vetter, “A Morphable Model for the Synthesis of 3D Faces,” in Proceedings of International Conference on Computer Graphics, pp. 187-194, 1999.
[39] W. T. Freeman and J. B. Tenenbaum, “Learning Bilinear Models for Two-Factor Problems in Vision,” CVPR, pp. 554-560, 1997.
[40] Y. Adini, Y. Moses, and S. Ullman, “Face Recognition: the Problem of Compensating for Changes in Illumination Direction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp.721-732, 1997.
[41] D. W. Jacobs, P. N. Belhumeur, and R. Barsi, “Comparing Images Under Variable Illumination,” CVPR, pp. 610-617. 1998.
[42] M. Savvides, B. V. K. V. Kumar, and P. K. Khosla, ““Corefaces”- Robust Shift Invariant PCA based Correlation Filter for Illumination Tolerant Face Recognition,” CVPR, vol. 2, pp.
834-841, 2004.
[43] 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.
[44] T. Vetter and T. Poggio, “Linear Object Classes and Image Synthesis from a Single Example Image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 733-742, 1997.
[45] S. Malassiotis and M. G. Strintzis, “Robust Face Recognition Using 2D and 3D Data:
Pose and Illumination Compensation,” Pattern Recognition, vol. 38, pp. 2537-2548, 2005.
[46] R. Gross, I. Matthews, and S. Baker, “Appearance-Based Face Recognition and Light-Fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, pp.
449-465, 2004.
[47] T.F. Cootes, G. Edwards, and C.J. Taylor, “Comparing Active Shape Models with Active Appearance Models,” in Proc. British Machine Vision Conference, vol. 1, pp. 173-182, 1999.
[48] V. Struc, B. Vesnicer, F. Mihelic, and N. Pavesic, “Removing Illumination Artifacts from Face Images Using the Nuisance Attribute Projection,” in IEEE ICASSP, pp. 846-849, 2010.
[49] Toolbox for illumination invariant face recognition (the INface toolbox), [Online].
Available: http://luks.fe.uni-lj.si/en/staff/vitomir/index.html
[50] M. A. Turk and A. P. Pentland, “Face Recognition Using Eigenfaces,” IEEE Proceedings of Computer Vision and Pattern Recognition, pp. 586-591, 1991.
[51] D. J. Jobson, Z. Rahman, and G. A. Woodell, “Properties and Performance of a Center/Surround Retinex,” IEEE Transactions on Image Processing, vol. 6, no. 3, pp.
451-462, 1997.
[52] J. G. Daugman, “Complete Discrete 2-D Gabor Transforms by Neural Networks for Image Analysis and Compression,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 7, pp. 1169-1179, 1988.
[53] L. Shen and L. Bai, “A review on Gabor wavelets for face recognition,” Pattern Analysis and Application, vol. 9, pp. 273-292, 2006.
[54] M. Yang and L. Zhang, “Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary,” ECCV, vol. 6, pp. 448-461, 2010.
[55] Y. Peng, A. Ganesh, J. Wright, W. Xu, and Y. Ma, “RASL: Robust Alignment by Sparse and Low-rank Decomposition for Linearly Correlated Images,” CVPR, pp.763-770, 2010.
[56] S. Yan, H. Wang, J. Liu, X. Tang, and T. S. Huang, “Misalignment-Robust Face Recognition,” IEEE Transactions on Image Processing, vol. 19, no. 4, pp. 1087-1096, 2010.
[57] L. Meylan and S. Susstrunk, “High Dynamic Range Image Rendering with a Retinex-Based Adaptive Filter,” IEEE Transactions on Image Processing, vol. 15, pp.
2820-2830, 2006.
[58] R. Gonzalez and R. Woods, Digital Image Processing, Prentice Hall, 3rd edition, 2008.
[59] H. Wang, S. Z. Li, and Y. Wang, “Face Recognition under Varying Lighting Conditions Using Self Quotient Image,” in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, pp. 819-824, 2004.
[60] W. Chen, M. J. Er, and S. Wu, “Illumination Compensation and Normalization for Robust Face Recognition Using Discrete Cosine Transform in Logarithmic Domain,” IEEE Transactions on Systems, vol. 36, pp. 458-466, 2006.
[61] W. T. Freeman and E. H. Edelson, “The Design and Use of Steerable Filters,” IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 9, pp. 891-906, 1991.
[62] S. J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An Interior-Point Method for Large-Scale l1-Regularized Least Squares,” IEEE Journal on Selected Topics in Signal Processing, pp. 606-617, 2007.
[63] T. Sim, S. Baker, and M.Bsat, “The CMU Pose, Illumination, and Expression Database,”
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1615-1618, 2003.
[64] AR Face Database, [Online]. Available:
http://www4.comp.polyu.edu.hk/~csmyang/Publication.html http://www2.ece.ohio-state.edu/~aleix/ARdatabase.html [65] AT&T ORL Face Database, [Online]. Available:
http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html