臺灣博碩士論文加值系統

本論文採用膚色偵測法找出彩色生活照片可能的人臉位置，透過賈伯小波(Gabor wavelet)抽取特徵進行類神經網路訓練，以判斷是否為人臉。在人臉辨識階段，先使用主動式外觀模型(active appearance model)和可導引濾波器(steerable filter)進行人臉正規化，接下來採用稀疏編碼(sparse coding)演算法，在五個訓練樣本下，生活照人臉辨識率可達80%，使用其他正面人臉資料庫(AR資料庫)辨識率更可高達98%。並提出直方圖統計法來減少稀疏編碼的權重數目為原來的60%，除了降低系統運算量，同時特徵向量仍然具有代表性。整體而言，可適用於家庭數位相簿管理，或數位相框之分類系統。

This thesis adopts skin-color model to find the candidate face region, then Gabor wavelets transformation is adopted to extract the entire face features. Afterward, neural network is trained to determine whether the candidate region is a human face or not. Finally, this thesis adopts active appearance model and steerable filter to normalize all faces for face recognition. Then this thesis implements sparse coding algorithm with 5 training faces to increase the face recognition rate up to 80% for photographs, and for frontal face of AR database also increases by 98%. Furthermore, this thesis proposes using histogram method to reduce 60% of sparse coding needed which also reduces the amount of system computational cost, and then the features are still representative. As a whole, this system is suitable for digital media classification of family photograph albums or digital photograph frames.

摘要　　　　　　 i
ABSTRACT ii
誌謝　　　　　　 iii
目錄　　　　　　 iv
圖目錄　　　　　　 vi
表目錄　　　　　　 viii
第一章 緒論 1
第二章 相關知識及理論 6
2.1人臉位置偵測 7
2.1.1 膚色位置偵測 7
2.1.2 人臉特徵抽取 9
2.1.3 類神經網路 10
2.2人臉正規化 11
2.2.1主動式外觀模型演算法 11
2.2.2光線明亮度正規化 14
2.3人臉辨識 23
第三章 人臉辨識系統 27
3.1人臉位置偵測方法 27
3.1.1 多重尺寸視窗搜尋 29
3.1.2 訓練類神經網路 29
3.2光線明亮度正規化濾波器 30
3.3稀疏編碼權重更新疊代演算法 30
第四章 實驗結果與分析 34
4.1 人臉偵測的實驗結果 34
4.2特徵人臉辨識率 38
4.3稀疏編碼人臉辨識率 42
4.4明亮度正規化對於辨識率的影響 49
4.5可導引濾波器正規化的人臉辨識率 50
4.6人臉解析度對於辨識率的影響 56
4.7生活照片人臉辨識率分析 56
4.8實驗結果與討論 57
第五章 結論與未來展望 59
參考文獻　　　　　　 60

[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