臺灣博碩士論文加值系統

人臉偵測與人臉辨認在監視系統、智慧型人機介面與安全資訊擷取上面非常有用。有非常多傑出的論文提出演算法解決人臉偵測與人臉辨認的問題。然而，非常少的論文考慮關於整合曝光控制、影像擷取、人臉追蹤與有效率人臉辨認的系統並且實現於行動的數位相機系統上。
本論文提出一個完整的監視系統並且實現於一個商業的數位相機產品上，本方法一開始先針對使用膚色機率模型偵測到的人臉進行調整曝光控制來確保臉部的曝光控制正確。接下來抽取出眼睛、鼻子與嘴唇的可能位置，然後正確的人臉位置就可以藉由特徵間相對位置的模組來確認。最後，被切割出的人臉區域使用DCT係數的執行人臉辨認並且使用支持向量機來分類。實驗結果顯示提出的系統很穩定並且可以達到及時的應用。

Face detection as well as recognition is particular useful for surveillance systems, intelligent human-computer interface and security information access. Many excellent algorithms have been proposed to solve the problems of face detection as well as recognition. However, very few of them considered the integration issues from exposure control, image capture, face tracking and efficient recognition for realizing such a system in portable camera system.

In this thesis, we propose a complete surveillance which is implemented on a commercial digital camera. The system first adaptively control the exposure based on the locations of human face which is detected by probability skin color model to ensure the right exposure of human face. The next step is to extract the possible locations of eyes, noses and lips. Then the right location of human face can be determined with relative location models. Finally the human faces are recognized based on DCT coefficients of the segmented image areas and classified with support vector machines. The experimental results show that the proposed system is stable and fast enough for real-time applications.

目錄 1
圖目錄 3
表目錄 4
第一章 緒論 5
1.1 研究動機 5
1.2 相關研究 7
1.2.1 人臉偵測相關研究 7
1.2.1.1 Knowledge-based methods 9
1.2.1.2 Feature-based methods 10
1.2.1.3 Template-matching methods 10
1.2.1.4 Appearance-based methods 11
1.2.2 人臉辨認相關研究 13
1.3 本論文提出之方法 14
1.4 論文架構 16
第二章 系統架構 17
2.1 硬體平台說明 17
2.1.1 ARM子系統 18
2.1.2 DSP子系統 19
2.1.3 影像週邊單元 19
2.1.4 記憶體控制單元 19
2.2 硬體流程說明 20
2.3 軟體系統架構圖 21
第三章 人臉偵測 26
3.1 相關研究 26
3.2 提出的方法 33
3.2.1 膚色偵測 33
3.2.2 使用輪廓搜尋演算法的膚色物件切割 35
3.2.3 臉部特徵抽取與驗證 37
3.3 以人臉為目標的自動曝光調整 40
第四章 特徵抽取與人臉辨認 42
4.1 特徵抽取與特徵統計分析 42
4.2 使用SVM的人臉辨認系統 48
4.2.1 SVM簡介 48
4.2.2 人臉辨認 54
第五章 實驗結果 55
5.1 人臉偵測 55
5.2 人臉辨認 58
第六章 結論與未來工作 59
6.1 結論 59
6.2 未來工作 59
參考文獻 60
附錄一 65
附錄二 80

[1] M. Yang, “Detecting faces in images,” IEEE Trans. Pattern Anal. Machine Intell., vol. 24, no. 1, pp. 34-58, Jan. 2002.
[2] H. A. Rowley, “Neural network-based face detection,” IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 1, pp. 23-38, Jan. 1998.
[3] C. Garcia and G. Tziritas, “Face detection using quantized skin color regions merging and wavelet packet analysis,” IEEE Trans. Meltimedia, vol. 1, no. 3, pp. 264-277, Sep. 1999.
[4] F. Tsalakanidou, S. Malassiotis and M. G. Strintzis, “Face localization and authentication using color and depth images,” IEEE Trans. Image processing, vol. 14, no. 2, pp. 152-168, Feb. 2005.
[5] K. K. Sung and T. Poggio, “Example-based learning for view-based human face detection,” IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 1, pp. 39-51, Jan. 1998.
[6] K. C. Lee, “Acquiring linear subspaces for face recognition under variable lighting,” IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 5, pp. 684-698, May 2005.
[7] C. A. Waring and X. Liu, “Face detection using spectral histograms and SVMs,” IEEE Trans. System, Man, Cybernetics, vol. 35, no. 3, June 2005.
[8] S. Z. Li, X. Lu, X. Hou, X. Peng, Q. Cheng, “Learning multiview face subspaces and facial pose estimation using independent component analysis,” IEEE Trans. Image Processing, vol. 14, no. 6, pp. 705-712, June 2005.
[9] R. L. Hsu, M. A. M, A. K. Jain, “Face detection in color images,” IEEE Trans. Pattern Anal., Machine Intell., vol. 24, no. 5, pp. 696-706, June 1999.
[10] C. H. Lin, J. L. Wu, “Automatic Facial Feature Extraction by genetic algorithm,” IEEE Trans. Image Processing, vol. 8, no. 6, pp. 834-845, June 1999.
[11] M. Pantic, L. J. M. Rothkrantz, “Facial action recognition for facial expression analysis from static face images,” IEEE Trans. System, Man, and Cybernetics, vol. 34, no. 3, pp. 1449-1461, June 2004
[12] J. Miao, B. Yin, K. Wang, L, Shen, X. Chen, “A hierarchical multiscale and multiangle system for human face detection in a complex background using gravity-center template,” Pattern Recognition, vol. 32, pp. 1237-1248, 1999.
[13] V. P. Kumar, T. Poggio, “Learning-based approach to real time tracking and analysis of faces,” Proc. Int. conf. on Automatic Face and Gesture recognition, pp. 96-101, March 2000.
[14] M. Zobel, A. Gebhard, D. Paulus, J. Denzler, H. Niemann, “Robust facial feature localization by coupled features,” Proc. IEEE conf. on Automatic Face and Gesture recognition, pp. 2-7, March 2000.
[15] Y. Wang, B. Yuan, “A novel approach for human face detection from color images under complex background,” Pattern Recognition, vol. 34, pp. 1983-1992, 2001.
[16] K. M. Lam, H. Yan, “An analytic-to-holistic approach for face recognition based on a single frontal view,” IEEE Trans. Pattern Anal. Machine Intell., vol. 20, no. 7, pp. 673-686, July 1998.
[17] C. H. Lee, J. S. Kim, K. H. Park, “Automatic human face location in a complex background using motion and color information,” Pattern Recognition, vol. 29, no. 11, pp. 1877-1889, 1996.
[18] P. J. Phillips, Y. Vardi, “Efficient illumination normalization of facial images,” Pattern Recognition Letters, vol. 17, pp. 921-927, 1996.
[19] H. Yao, W. Gao, “Face detection and location based on skin chrominance and lip chrominance transformation from color images,” Pattern Recognition, vol. 34, pp. 1555-1564, 2001.
[20] G. Wei, I. K. Sethi, “Face detection for image annotation,” Pattern Recognition Letters, vol. 20, pp. 1313-1321, 1999.
[21] L. L. Huang, A. Shimizu, Y. Hagihara, H. Kobatake, “Face detection from cluttered images using polynomial neural network,” Pattern Recognition, vol. 51, pp. 197-211, 2003.
[22] S. H. Jeng, H. Y. M. Liao, C. C. Han, M, Y. Chern, Y. T. Liu, “Facial featre detection using geometrical face model: an efficient approach,” Pattern Recognition, vol. 31, no. 3, pp. 273-282, 1998.
[23] H. Sako, M. Whitehouse, A. Smith, A. Suherland, “Real-time facial-feature tracking based on matching techniques and its applications,” Proc. Int. Conf. 12th IAPR, vol. 2, pp. 320-324, Oct. 1994.
[24] Y. Adini, Y. Moses, S. Ullman, “Face recognition: the problem of compensating for changes in illumination direction,” IEEE Trans. Pattern Anal. Machine Intell., vol. 19, no. 7, pp. 721-732, July 1997.
[25] P. W. Hallinan, “A low-dimensional representation of human faces for arbitrary lighting condictions,” Proc. Int. conf. CVPR’94, pp. 995-999, June 1994
[26] P. N. Belhumeur, J. P. Hespanha, D. J. Kriegman, “Eigenfaces vs. fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Machine Intell., vol. 19, no. 7, pp. 711-720, July 1997.
[27] A. M. Martinez, A. C. Kak, “PCA versus LDA,”, IEEE Trans. Pattern Anal. Machine Intell., vol. 23, no. 2, pp. 228-233, Feb. 2001.
[28] W. Chen, M. J. Er, S. Wu, “Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain,” IEEE. Trans. System, Man, and Cybernetics, vol. 36, no. 2, pp. 458-466, April 2006.
[29] S. Pang, D, Kim, S. Y. Bang, “Face Membership authentication using SVM classification tree generated by membership-based LLE data partition,” IEEE Trans. Neural Networks, vol. 16, no. 2, pp. 436-446, March 2005.
[30] M. J. Er. W. Chen. S. Wu, “High-speed face recognition based on discrete consine transform and RBF neural networks,” IEEE Trans. Neural Networks, vol. 16, no. 3, pp. 679-691, May 2005.
[31] X. Xie, K. M. Lam, “Face recognition under varying illumination based on a 2D face shape model,” Pattern Recognition, vol. 38, pp. 221-230, 2005.
[32] M. Hamouz, J. Kittler, J. -K, Kamarainen, P. Paalanen, H. Kalviainen, J. Matas, “Feature-based affine-invariant localization of faces,” IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 9, pp. 1490-1495, Sept. 2005.
[33] K. I. Kim, K. Jung. H. J. Kim, “Face recognition using kernel principal component analysis,” IEEE Signal Processing Letters, vol. 9, no. 2, pp. 40-42. Feb. 2002.
[34] X. He, S. Yan, Y. Hu, P. Niyogi, H. J. Zhang, “Face recognition using laplacianfaces,” IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 3, pp. 328-340, March 2005.
[35] B. G. Park, K. M. Lee, S. U. Lee, “Face recognition using face-ARG matching,” IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 12, pp. 1982-1988, Dec. 2005.
[36] K. Venkataramani, S. Qidwai, B. V. K. Vijayakumar, “Face authentication from cell phone camera images with illumination and temporal variations,” IEEE Trans. System, Man, and Cybernetics, vol. 35, no. 3. pp. 411-418, Aug. 2005.
[37] J. Ruiz-del-Solar, P. Navarrete, “Eigenspace-based face recognition: a comparative study of different approaches,” IEEE Trans. System, Man, Cybernetics, vol. 35, no. 3, pp. 315-325, Aug. 2005.
[38] J. Kim, J. Choi, J. Yi, M. Turk, “Effictive representation using ICA for face recognition robust to local distortion and partial occlusion,” IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 12, pp. 1977-1981, Dec. 2005
[39] X. Wang, X. Tang. “A unified framework for subspace face recognition,” IEEE Trans. Pattern Anal. Machine Intell., vol. 26, no. 9. pp. 1222-1228, Sept. 2004.
[40] H. Cevikalp, M. Neamtu, M. Wilkes, A. Barkana, “Discriminative common vectors for face recognition,” IEEE Signal Processing Letters, vol. 27, no. 1, pp. 4-13, Jan. 2005.
[41] F. Zuo, P. H. N. de With, “Real-time embedded face recognition for smart home,” IEEE Trans. Consumer Electronics, vol. 51, no. 1, Feb. 2005.
[42] F. Perronnin, J. L. Dugelay, K. Rose, “A probabilistic model of face mapping with local transformations and its application to person recognition,” IEEE Trans. Pattern Anal. Machine Intell., vol. 27, no. 7, pp. 1157-1171, July 2005.
[43] A. N. Rajagopalan, R. Chellappa, N. T. Koterba, “Background learning for robust face recognition with PCA in the presence of cluster,” IEEE Trans. Image Processing, vol. 14, no. 6, June 2005.
[44] H. Ai, L. Liang, G. Xu, “Face detection based on template matching and support vector machines,” Proc. Int. conf. Image Processing, vol. 1, pp. 1006-1009, Oct. 2001.
[45] Y. Hori, K. Shimizu, Y. Nakamura, T. Kuroda, “A real-time multi face detection technique using positive-negative lines-of-face template,” Proc. Int. conf. ICPR’04, vol. 1, pp. 765-768, Aug. 2004.
[46] J. W. Kim, B. H. Kang, P. M. Kim, M. S. Cho, “Human face location in image sequences using genetic templates,” Proc. Int. conf. System, Man, cybernetics, vol. 3, pp. 2985-2988, Oct. 1997.
[47] Y. Dai, Y, Nakano, “Face-texture model based on SGLD and its application in face detection in a color scene,” Pattern Recognition, vol. 29, no. 6, pp. 1007-1017, 1996.
[48] B. Menser, F. Muller, “Face detection in color images using principal components analysis,” Proc. Int. conf. Image Processing and its application, vol. 2, pp. 620-624, July 1999.
[49] A. Pentland, B. Moghaddam, T. Starner, “View-based and modular eigenspaces for face recognition,” Proc. Int. conf. CVPR’94, pp. 84-91, June 1994.
[50] B. Moghaddam, A. Pentland, “Probabilistic visual learning for object representation,” IEEE Trans. Pattern Anal. Machine Intell., vol. 19, no. 7, pp. 696-710, July 1997.
[51] P. Chellappa, C. L. Wilson, S. Sirohey, “Human and machine recognition of faces: a survey,” Proc. IEEE, vol. 83, no. 5, pp. 705-741, May 1995.
[52] TMS320DM310 digital media DSP Technical Reference Manual, Version2.0, Texas Instruments, 2003.