臺灣博碩士論文加值系統

近幾年來，快速的影像辨識處理為嵌入式系統必備的功能，例如嵌入式車載系統、安全監控系統、個人攜帶式設備，因此即時性的影像處理速度是必備的功能之一，可是當影像尺寸越來越大的時候，偵測速度會大幅地下降，同時人臉的誤報率也會上升。在本篇論文中，我們基於布斯特演算法結合膚色過濾提出了一個快速且具有高偵測率的人臉偵測方法，我們使用YCbCr色彩空間進行膚色區域的偵測，藉由這個方法，我們過濾了圖片中大部分不需要被偵測的區域，只留下膚色區域進行偵測，再將影像輸入到我們的人臉偵測系統，如此我們可以大幅減少計算量及運算時間，同時又能夠大幅降低人臉的誤報率，達到高準確率且低誤判率的人臉偵測系統。
此外，我們將提出的人臉偵測系統進行硬體實現，實驗結果顯示出在輸入影像尺寸為640x480之下，我們提出的硬體架構可以得到高偵測率(80.5%)且快速的人臉偵測(22 FPS)。

In recent years, fast processing of image recognition is required for embedded systems, e.g., embedded automotive systems, security systems, or portable device. Face detection is the most important step in face recognition systems with applications to face tracking and recognition. Unfortunately, when frame size increase, the speed of face detection will extremely decrease and false positive rate will increase. In this thesis, we implement the rapid and high correct rate face detection method based on Adaboost algorithm. Also, we choose YCbCr color space to filter pixels of the entire image first and to detect the faces of image. By this way, we can remove the most parts of the image which don’t need to be detected in order to reduce the false detection rate.
In addition, the hardware architecture for face detection system is implemented.
Experimental results show that the proposed architecture can achieve high detection rate (80.5 %) at 22 frame/second for image size of 640 × 480 pixels.

ABSTRACT I
ACKNOWLEDGMENT III
CONTENTS IV
LIST OF TABLES VI
LIST OF FIGURES VII

Chapter 1 Introduction..1
1.1 Preliminary..1
1.2 Motivation..4
1.3 Thesis Organization..5

Chapter 2 Background..6
2.1 Evolution of Face Detection Research..6
2.2 Knowledge-Based Methods..7
2.3 Template Matching Methods..8
2.4 Feature-Based Methods..10
2.4.1 Texture..11
2.4.2 Skin Color..12
2.5 Appearance-Based Methods..13
2.5.1 Bayesian Classification..13
2.5.2 Eigenfaces..14
2.5.3 Principal Component Analysis..15
2.5.4 Neural Networks..16

Chapter 3 Adaboost Algorithm..18
3.1 Summary of Adaboost Algorithm..18
3.2 Haar-like Features..19
3.3 Integral Image..20
3.4 Adaboost Learning Algorithm..24
3.5 The Cascaded Classifier..28

Chapter 4 Proposed Algorithm and
Architecture Implementation..34
4.1 Review of Skin Color Detection..34
4.2 The RGB Color Space..36
4.3 The HSV Color Space..38
4.4 The YCbCr Color Space..39
4.5 Proposed Algorithm of Face Detection..41
4.6 Adaboost Training..42
4.7 Hardware Implementation..49
4.7.1 Frame Buffer, Line Buffer and Sub-window Registers..49
4.7.2 Skin Color Filter..54
4.7.3 Integral Image Generator..56
4.7.4 Classifier Stage Process..57
4.7.5 Feature Bank and Detected Region Coordinate Unit..59
4.7.6 Partially Parallel Method..60

Chapter 5 Experimental Results..64

Chapter 6 Conclusions..79

REFERENCES..80

[1] Ming-Hsuan Yang, David J. Kriegman, Narendra Ahuja(2002), “Detecting Faces in Images: A Survey in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1.
[2] Yoav Freund and Robert E. Schapire(1997), “A decision-theoretic generalization of On-line learning and an application to boosting, journal of computer and system sciences 55, 119-139.
[3] T. Sakai, M. Nagao, and T. Kanade(1972),“Computer analysis and classification of photographs of human faces, First USA—Japan Computer Conference, p.27.
[4] R. Chellappa, C. L. Wilson, and S. Sirohey(1995),“Human and machine recognition of faces : A survey, Proc. IEEE 83, 5.
[5] Erik Hjelmås and Boon Kee Low(2001), “Face Detection: A survey, Computer Vision and Image Understanding, vol.83(3), pp. 236-274.
[6] M.-H. Yang, D. J. Kriegman and N. Ahuja(2002), “Detecting Faces in Images: A Survey, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.24(I), pp. 34 – 58.
[7] Yongzhong Lu, Jingli Zhou, Shengsheng Yu(2003), “A SURVEY OF FACE DETECTION, EXTRACTION AND RECOGNITION, Computing and Informatics, Vol. 22.
[8] G. Yang and T. S. Huang(1994), “Human Face Detection in Complex Background, Pattern Recognition, vol.27, no.1, pp.53-63.
[9] M.F. Augusteijn and T.L. Skujca(1993), “Identification of Human Faces through Texture Based Feature Recognition and Neural Network Technology, Proc. IEEE Conf. Neural Networks, pp. 392-398.
[10] Wei Sun, Weigong Zhang, Xiaorui Zhang, Gang Chen and Chengxu Lv(2010), “Multi-feature Fusion Method of Driver Face Location Based on Area Coincidence Degree and Prior Knowledge International Conference on, PACCS '09, pp.431-434.
[11] Ikeda O.(2003), “Segmentation of faces in video footage using HSV color for face detection and image retrieval, International Conference on Image Processing, Volume 3, 913-6.
[12] R. L. Hsu, M. Abdel-Mottaleb and AK Jain.(2002), “Face detection in color images, IEEE Trans. Pattern Analysis and Machine Intelligence, pp. 696-706.
[13] Varsha Powar, Aditi Jahagirdar and Sumedha Sirsikar(2001), “Skin Detection in YCbCr Color Space, International Journal of Computer Applications (0975 –8887).
[14] J.J de Dios, N. Garcia(2003), “Face detection based on a new color space YCgCr, ICIP03, vol.2, pp III-909-12.
[15] H. Schneiderman and T. Kanade(1998), “Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition Proc. IEEE Conf. Computer Vision and Pattern Recognition,pp. 45-51.
[16] H. Rowley, S. Baluja, and T. Kanade(1996), “Human Face Detection in Visual ScenesAdvances in Neural Information Processing Systems 8, D.S. Touretzky, M.C. Mozer, and M.E. Hasselmo, eds., pp. 875-881.
[17] H. Rowley, S. Baluja, and T. Kanade(1996), “Neural Network-Based Face Detection , Proc. IEEE Conf. Computer Vision and Pattern Recognition,pp. 203-208.
[18] H. Rowley, S. Baluja, and T. Kanade(1998), “Neural Network-Based Face Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38.
[19] H. Rowley, S. Baluja, and T. Kanade(1998), “Rotation Invariant Neural Network-Based Face Detection,Proc. IEEE Conf. Computer Vision and Pattern Recognition,pp. 38-44.
[20] P. Viola, and M. J. Jones(2004), “Robust Real-Time Face Detection, International Journal of Computer Vision 57(2), 137–154.
[21] Lienhart R., Liang L. and Kuranov A.(2003), “A Detector Tree of Boosted Classifiers for Real-time Object Detection and Tracking, Microcomputer Research Labs, Intel Corperation, Santa Clara, CA., vol. 2, pp.II-277-80.
[22] Nidhi Tiwari1, Mohd. Ahmed2(2013), “Detection of facial features of frontal face color images, International Journal of Advanced Computer Research, Vol.3, Number-3 Issue-1.
[23] P. Kakumanu, S. Makrogiannis, N. Bourbakis(2007), “A Survey of Skin-Color Modeling and Detection Methods, Pattern Recognition 40, pp.1106-1122.
[24] V. Vezhnevets and V. Sazonov and A. Andreeva(2003), “A survey on pixel-based skin color detection techniques, 13th International Conference on the Computer Graphics and Vision.
[25] J. J. de Dios, N. Garcia(2003), “Face detection based on a new color space YCgCr, International Conference on Image Processing, vol.2,909-12.
[26] Jensen, O. H.(2008), “Implementing the Viola-Jones face detection algorithm, MSc thesis, Technical University of Denmark.
[27] R. Lienhart and J. Maydt.(2002), “An extended set of Haar-like features for rapid object detection, in Proc. IEEE Int’l Conf. Image Processing, volume 1.
[28] P. Viola and M. Jones(2002), “Robust real-time object detection International Journal of Computer Vision, 57(2):137–154.
[29] Y. Wei, X. Bing, and C. Chareonsak(2004), “FPGA implementation of AdaBoost algorithm for detection of face biometrics, in Proc. IEEE Int’l Workshop Biomedical Circuits and Systems, page S1.
[30] M. Yang, Y. Wu, J. Crenshaw, B. Augustine, and R. Mareachen(2006), “Face detection for automatic exposure control in handheld camera In Proc. IEEE Int’l Conf. Computer. Vision Systems, page 17.
[31] Masayuki Hiromoto, Kentaro Nakahara and Hiroki Sugano(2007), “A Specialized Processor Suitable for AdaBoost-Based Detection with Haar-like Features, IEEE Conference on Computer Vision and Pattern Recognition.
[32] Y. Ming, J. Crenshaw, B. Augustine, and R. Mareachen, W. Ying(2010), “AdaBoost-based face detection for embedded systems in Computer Vision and Image Understanding, vol. 114(11), pp. 1116-1125.
[33] J. Cho, S. Mirzaei, J. Oberg, R. Kastner(2009), “FPGA-Based face detection system haar classifiers, in 7th ACM SIGDA International Symposium on Field-Programmable Gate Arrays, pp.103-111.
[34] Duy Nguyen, David Halupka, Parham Aarabi and Ali Sheikholeslami(2006), “Real-Time Face Detection and Lip Feature Extraction Using Field-Programmable Gate Arrays, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART B: CYBERNETICS, VOL. 36, NO. 4.
[35] Chun He1, Alexandros Papakonstantinou and Deming Chen(2009), “A Novel SoC Architecture on FPGA for Ultra Fast Face Detection, IEEE International Conference on Computer Design, 412-418.