人臉辨識的相關研究已發展多年，在市面上也存在許多應用人臉辨識技術的系統，像是出入口監控系統、出入境管制系統甚至是學生考勤系統等，應用人臉辨識的系統通常需要事先建立資料庫的方式，但是在實際場景中架設出入口門禁系統所需的臉部資訊相當大量，造成事前建立資料庫需耗費大量人力資源，有鑑於此，本篇論文提出了ㄧ套能自動學習的即時臉部辨識系統，擺脫傳統臉部辨識需要事先建立資料庫的方式，利用原有的出入口刷卡式門禁管制結合人物追蹤方法，自動擷取臉部樣本資訊，再訓練特徵後提供系統辨識，此方法除了減少建立資料庫的人力資源外，在相似的場景下擷取到的臉部資訊能提升辨識率，還能不斷更新臉部資訊以因應人物外觀上的變化。

Face recognition has matured over the recent years, it is widely used in access control system, immigration control system even lecture attendance system. Traditional face recognition system needs to build a database in advance. When a large company wants to build a face recognition system, they usually pay a huge effort on building the database. In this paper, we propose a real-time face recognition system with auto-learning via a access card. The system can detect and track face in real-time from the existing camera. Our system can build database automatically without any user operation and update database automatically for self-learning.

論文審定書 i
誌謝 ii
摘 要 iii
Abstract iv
目 錄 v
圖目錄 vi
表目錄 vii
第一章、 簡介 1
1.1、 論文概述 1
1.2、 論文貢獻 2
1.3、 論文架構 3
第二章、 文獻探討 4
2.1、 人臉偵測 4
2.2、 物件追蹤 6
2.3、 特徵提取與臉部辨識 7
第三章、 研究方法 9
3.1、 臉部偵測 10
3.1.1. 高斯混和模型 11
3.1.2. 形態學中的膨脹及侵蝕 12
3.1.3. 膚色偵測 13
3.1.4. 連通組合法 14
3.1.5. 移動膚色判讀及遮罩擴充 15
3.1.6. 積分影像 16
3.2、 物件追蹤 18
3.3、 特徵提取與識別 21
第四章、 系統建置 24
4.1、 系統環境與操作介面 24
4.2、 臉部偵測 28
4.3、 物件追蹤 33
4.4、 臉部辨識 34
第五章、 結論與未來研究方向 39
參考文獻 40

[1] J. Watada, Z. Musa, L. C. Jain and J. Fulcher, “Human Tracking: A State-of-Art Survey,” Knowledge-Based and Intelligent Information and Engineering Systems, pp. 454-463, 2010.
[2] A. Rajpurohit, A. Agarwal, M. Gaikwad, K. Garg and V. Inamdar, “Securing Public Places Using Intelligent Motion Detection,” IEEE International Conference on Engineering Education: Innovative Practices and Future Trends(AICERA 2012), pp. 1-4, 2012.
[3] F. C. Cheng and S. J. Ruan, “Accurate Motion Detection Using a Self-Adaptive Background Matching Framework,” IEEE Transactions on Intelligent Transportation Systems, pp. 671-679, 2012.
[4] G. Farneb¨ack, “Two-Frame Motion Estimation Based on Polynomial Expansion,” Image Analysis Lecture Notes in Computer Science, vol. 2749, pp. 363-370, 2003.
[5] F. Chang, G. Zhang, X. Wang and Z. Chen, “PTZ Camera Target Tracking in Large Complex Scenes,” World Congress on Intelligent Control and Automation, pp. 2914-2918, 2010.
[6] P. Kumar, A. Dick and T. S. Sheng, “Real Time Target Tracking with Pan Tilt Zoom Camera,” Digital Image Computing: Techniques and Applications, pp. 492-497, 2009.
[7] N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886-893, 2005.

[8] P. KaewTraKulPong, R. B. Vision and Virtual Reality group, “An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection,” 2nd European Workshop on Advanced Video Based Surveillance Systems, AVBS01, 2001.
[9] M. H. Yang, D. J. Kriegman and N. Ahuja, “Detecting Faces in Images: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 24.1, pp. 34-58, 2002.
[10] V. S. Bhat, J.D. Pujari and Bhavana, “A Hybrid Skin Color Model for Face Detection,” International Journal of Engineering Research and General Science Vol. 2.2, 2014.
[11] P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” Accepted Conference on Computer Vision and Pattern Recognition, 2001.
[12] P. Viola and M. Jones, “Robust Real-Time Face Detection,” International Journal of Computer Vision Vol. 57.2, pp. 137 - 154, 2004.
[13] C. P. Papageorgiou, M. Oren and T. Poggio, “A General Framework for Object Detection,” IEEE Sixth International Conference on Computer Vision, pp. 555-562, 1998.
[14] Y. Freund and R. E. Schapire, “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” Journal of Computer and System Sciences Vol. 55.1, pp. 119-139, 1997.
[15] U. Bakshi and R. Singhal, “A Survey on Face Detection Methods and Feature Extraction Techniques of Face Recognition, ” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Vol. 3.3, 2014.
[16] L. Wiskott and C. von der Malsburg, “Recognizing Faces by Dynamic Link Matching, ” Neuroimage, vol. 4, pp. 514- 518, 1996
[17] T. Ojala, M. Pietikäinen and T. Mäenpää, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 24.7, pp. 971-987, 2002.
[18] T. Ojala, M. Pietikäinen and D. Harwood, “A Comparative Study of Texture Measures with Classification based on Featured Distributions,” Pattern Recognition Vol. 29.1, pp. 51-59, 1996.
[19] R. Duda and P. Hart, “Pattern Classification and Scene Analysis,” New York: Wiley, 1973
[20] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projecti,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997.
[21] V. A. Oliveira and A. Conci, “Skin Detection using HSV Color Space,” H. Pedrini, J. Marques de Carvalho, Workshops of Sibgrapi, pp. 1-2, 2009.
[22] Avail at http://www.vision.jhu.edu/teaching/vision08/index.htm, Lecture No.22-2
[23] M. S. Bartlett, J. R. Movellan and T. J. Sejnowski, “Face Recognition by Independent Component Analysis,” IEEE Transactions on Neural Networks, Vol. 13, No. 6, 2002
[24] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 2, 2009
[25] V. E. Liong, J. Lu, and G. Wang, “Face Recognition Using Deep PCA,” Information, 9th International Conference on Communications and Signal Processing (ICICS), 2013
[26] B. Amos, B. Ludwiczuk and M. Satyanarayanan, “OpenFace: A General-
Purpose Face Recognition Library with Mobile Applications,” IEEE Winter Conference on Applications of Computer Vision (WACV), 2016