臺灣博碩士論文加值系統

身分確認在二十一世紀的資訊化社會中將扮演愈來愈重要的角色，如何構建一套既安全又便捷的身分確認系統是目前學界與業界都很熱衷的研究課題。本篇論文介紹一個我們所提出的個人化人臉身分確認系統。此系統的訓練可以分成三個階段，分別為初始訓練、現場訓練及現場評估。在初始訓練中，資料庫裡的人臉資料先經過主成份分析，投影到較小的子空間，然後再用集群分析將之分成若干集群，讓彼此相似的人臉資料分到同一集群。在現場訓練階段中，我們的系統將擷取一些系統主人的臉部資料，並找出和系統主人相似的集群，並將之分配到該集群中；其後在該集群內運用主成份分析和線性鑑別分析訓練出一個較能分辨系統主人之人臉資料的子空間。最後在現場評估階段，系統將利用更多的系統主人之人臉資料，加上在資料庫中扮演入侵者的人臉資料，進行兩階段的評估，以決定出適當的門檻值。在系統的操作與線上學習部分，我們讓使用者可以在確認失敗時，可以經由輸入密碼的方式，讓系統取得系統主人更多的人臉訓練資料，用以修改鑑別子空間及門檻值，以期能使下次的身分確認能有更高的正確率。我們使用三種不同的特徵距離來作測試，結果均顯示以集群為基礎的線性鑑別分析比傳統的線性鑑別分析可以獲得更高的正確率。

Recently, person authentication becomes more and more important as technology advances. How to build a safe and convenient identity verification system is a hot research topic in academia and business. In this thesis, we introduce a personalized face verification system based on cluster dependent LDA subspace. The training of the system can be divided into three parts: the initial training, on-site training, and on-site evaluation. In the initial training, we select some human face images of our database as representative face images. The images can be clustered by using K-means clustering method. For on-site training, the client must give some face images for on-site training. We can assign the client to the closest cluster. To separate the client from other representative people in the cluster, we will adopt LDA method to the LDA subspace. At last, we use information of the client and impostors to adjust the threshold. In the part of system operation and on-line training, we can manually input the password when we cannot verified by the system. The system can get more training images to retain the LDA subspace and threshold. We also compare three different matching scores. The experimental results show our method outperforms the traditional LDA method。

CHAPTER 1. INTRODUCTION 7
1.1 MOTIVATION 7
1.2 PREVIOUS WORKS 9
1.3 OUR WORK 11
CHAPTER 2. RELATED WORKS 13
2.1 REVIEW OF PCA 13
2.2 REVIEW OF LDA 15
2.3 SMALL SAMPLE SIZE PROBLEM 17
CHAPTER 3. PERSONALIZED FACE VERIFICATION SYSTEM BASED ON CLUSTER-DEPENDENT LDA-SUBSPACE 20
3.1 INTRODUCTION 20
3.2 SYSTEM TRAINING 21
3.2.1 Initial Training 21
3.2.2 On-Site Training 24
3.2.3 On-Site Evaluation 26
3.3 SYSTEM OPERATION AND ON-LINE TRAINING 27
CHAPTER 4. EXPERIMENT 30
4.1 EXPERIMENT SETUP 30
4.2 EXPERIMENT RESULT AND DISCUSSION 33
CHAPTER 5. CONCLUSION AND FUTURE WORK 38
5.1 CONCLUSION 38
5.2 FUTURE WORK 39
BIBLIOGRAPHY 41

[1] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997.
[2] R. Brunelli and T. Poggio, “Face Recognition: Features versus Templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no.10, pp. 1042-1052, 1993.
[3] R. Chellappa, C. L. Wilson, and S. Sirohey, “Human and Machine Recognition of Faces: A Survey,” Proceedings of IEEE, vol. 33, no. 5, pp. 705-740, 1995.
[4] L. F. Chen, H. Y. M. Liao, J. C. Lin, M. T. Ko, and G. J. Yu, “A New LDA-based Face Recognition System Which Can Solve the Small Sample Size Problem,” Pattern Recognition, vol. 33, no. 10, pp. 1713-1726, 2000.
[5] D. S. R. Duda, and P. Hart, Pattern Classification, Wiley, New York, 2001.
[6] J. L. Dugelay, J. C. Junqua, C. Kotropoulos, R. Kuhn, F.Perronnin, and I. Pitas, “Recent Advances in Biometric Person Authentication,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, vol. 4, pp. 4060-4063, 2002.
[7] K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, New York, 1990.
[8] R. A. Fisher, “The Statistical Utilization of Multiple Measurement,” Annals of Eugenics, vol. 8, pp. 337-386, 1938.
[9] A. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643-660, 2001.
[10] R. Huang, Q. Liu, H. Lu, and S. Ma, “Solving the Small Sample Size Problem of LDA,” Proceedings of International Conference on Pattern Recognition, Quebec City, Canada, vol. 3, pp. 29-32, 2002.
[11] A. K. Jain, L. Hong and S. Pankanti, “Biometric: Promising Frontiers for Emerging Identification Market,” Communications of ACM, vol. 2, pp. 91-98, 2002.
[12] C. Kotropoulos, A. Tefas, and I. Pitas, “Frontal Face Authentication Using Morphological Elastic Graph Matching,” IEEE Transactions on Image Processing, vol. 4, no. 9, pp. 555-560, 2000.
[13] Y. Li, J. Kittler, and J. Matas, “Effective Implementation of Linear Discriminant Analysis for Face Recognition and Verification,” In A. Leonardis and F. Solina, editors, Proceedings of International Conference on Computer Analysis and Patterns, Ljubljana, Slovenia, pp.234-242, 1999.
[14] Y. Li, J. Kittler, and J. Matas, “On Matching Scores of LDA-based Face Verification,” Proceedings of British Machine Vision Conference, London, UK, vol. 1, pp. 42-51, 2000.
[15] J. Luettin and G. Maître, “Evaluation Protocol for the Extended M2VTS Database (XM2VTSDB),” Technical Report IDIAP-COM 98-05, IDIAP, 1998.
[16] M. Lades, J. C. Vorbrüggen, J. Buhmann, J. Lange, C. v. d. Malsburg, R. P. Würtz, and W. Konen, “Distortion invariant object recognition in the dynamic link architecture,” IEEE Transactions Computation, vol. 42, pp. 300—311, 1993.
[17] J. Matas, M. Hamouz, K. Jonsson, J. Kittler, Y. Li, C. Kotropoulos, A. Tefas, I. Pitas, T. Tan, H. Yan, F. Smeraldi, J. Bigün,, N. Capdevielle, W. Gerstner, S. Ben-Yacoub, Y. Abdeljaoued, E. Mayoraz, “Comparison of Face Verification Results On the XM2VTFS Database” Proceedings of International Conference on Pattern Recognition, Barcelona, Spain, vol. 4, pp. 858-863, 2000.
[18] A. M. Martinez and A. C. Kak, “PCA versus LDA,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, 2001.
[19] K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre, “XM2VTSDB: The Extended M2VTS Database,” International Conference on Audio and Video-based Biometric Person Authentication, Washington DC, pp. 72-77, 1999.
[20] B. Moghaddam and A. Pentland, “Probabilistic Visual Learning for Object Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, pp. 696-710, 1997.
[21] C.B. Moler and G. Stewart, “An Algorithm for Generalized Matrix Eigenvalue Problem ,” SIAM Journal of Numerical Analysis, vol. 10, pp. 99-130, 1973.
[22] P. J. Phillips, H. Moon, P. Rauss, and S. Rizvi, “The FERET Evaluation Methodology for Face Recognition Algorithm,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, pp. 137-143, 1997.
[23] P. J. Phillips, H. Moon, S. Rizvi, and P. Rauss, “The FERET Evaluation Methodology for Face Recognition Algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, pp. 1090-1104, 2000.
[24] D. L. Swets and J. Weng, “Using Discriminant Eigenfeatures for Image Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 831-837, 1996.
[25] M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[26] L. Wiskott, J. M. Fellous, and C. von der Malsburg, “Face Recognition by Elastic Bunch Graph Matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 775-779, 1997.
[27] Ming-Hsuan Yang, “Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods,” Proceedings of International Conference on Automatic Face and Gesture Recognition, Washington D. C., pp. 215-220, 2002.
[28] J. Yang, Y. Yu, and W. Kunz, “An Efficient LDA Algorithm for Face Recognition,” Proceedings of International Conference on Control, Automation, Robotics, and Vision, Singapore, pp. 2067-2070, 2000.
[29] W. Zhao, R. Chellappa, A. Rosenfeld, and P. J. Phillips, “Face Recognition: A Literature Survey,” Technical Report CAR-TR-948, CS-TR-4167, N00014-95-1-0521, University of Maryland, 2000.
[30] W. Zhao, R. Chellappa, and A. Krishnaswamy, “Discriminant Analysis of Principal Components for Face Recognition,” Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, pp. 336-341, 1998.
[31] W. Zhao, R. Chellappa, and P. J. Phillips, “Subspace Linear Discriminant Analysis for Face Recognition,” Technical Report CAR-TR-914, Center for Automation Research, University of Maryland, 1999.
[32] J. Zhang, Y. Yan, and M. Lades, “Face Recognition: Eigenface, Elastic Matching, and Neural Nets,” Proceedings of IEEE, vol. 85, no. 9, pp. 1423-1435, 1997.