在電腦視覺的研究領域中，人臉辨識是非常重要的研究課題之一。以往都是以PC base來做相關的研究，但是中央處理器除了要擷取影像資料外，還要執行視窗作業系統，這些程序往往會造成電腦內的中央處理器龐大負擔，因而導致整個系統效率降低。所以本論文利用德州儀器生產的數位訊號處理器TMS320DM642 EVM結合CCS3.0的軟體系統為開發平台。首先利用EDMA控制器開啟DMA通道，將影像資料直接存入預設好的緩衝區內等待處理，接著使用背景影像相減法得到移動的膚色物體區塊，再利用橢圓遮罩找出人臉的位置，然後根據線性鑑別式分析的原理來抽取特徵參數，最後使用最小歐式距離的決策方式，來進行判定，而處理完畢的影像，也透過EDMA控制器將其結果輸出在螢幕上。

In the field of computer vision, face recognition is one of important researches. We often use PC base to do relevant researches, but its CPU not only captures image materials but also executes Windows Operating System. These procedures would cause the huge loading of the CPU and the lower efficiency of the system, so we change to combine the TI TMS320DM642 EVM with CCS 3.0 (Code Composer Studio 3.0) software system to be the developing platform. First, we use the EDMA (Enhanced Direct Memory Access) controller to create a DMA gateway, and directly store image materials in the capture buffer to wait for the process. Then we use Background Image Subtraction Algorithm to get the moving objects block which have the color of skin and use an ellipse mask to locate the face. Next, we use LDA (Linear Discriminate Analysis) to get the feature vector. Finally, we employ the minimum Euclidean distance to determine the most likely person and export the processed images on the screen by the EDMA controller.

[1] 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, No. 1, pp. 34-58, 2002.
[2] S. Khan and M. Shah, “Tracking People in Presence of Occlusion,” Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan, pp. 1132-1137, January 2000.
[3] I. Haritaoglu, D. Harwood, and L.S. Davis, “Hydra: Multiple People Detection and Tracking Using Silhouettes,” Proceedings of the Second IEEE Workshop on Visual Surveillance, pp. 6-13, June 26, 1999.
[4] I. Haritaoglu, D. Harwood, and L. Davis, “W4: Who, When, Where, What: A Realtime System for Detecting and Tracking People,” IEEE International Conference on Automatic Face and Gesture Recognition, pp. 222–227, April 1998.
[5] C.R. Wren, A. Azarbayejani, T. Darrell, and A. Pentland, “Pfinder: Realtime Tracking of the Human Body,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, 780–785, 1997.
[6] Texas Instruments Inc., “TMS320DM642 Technical Overview”.
[7] Texas Instruments Inc., “TMS320C64x DSP Two-Level Internal Memory Reference Guide”.
[8] K.S. Kah, “Learning and Example Selection for Object and Pattern Detection“ PhD thesis, Massachusetts Institute of Technology Artificial Intelligence Laboratory, January 1996. Available as AI Technical Report 1572.
[9] M.J. Swain and D.H. Ballard, “Colour Indexing,” International Journal of Computer Vision, Vol. 7, pp. 11-32, 1999.
[10] C.H. Lin and J.L. Wu, ”Automatic facial feature extraction by genetic algorithms,” IEEE Transactions on Image Process, Vol. 86, pp. 834-845, June 1999.
[11] X. Xie, R. Sudhakar, and H. Zhuang, “A Cascaded Scheme for Tracking and Head Movement Compensation, “IEEE Trans. Systems, Man, and Cybernetics-Part A: Systems and Human, Vol. 28, no. 4, pp. 487-490, July 1998.
[12] F. Preteux and M. Malciu, “Model-based Head Tracking and 3D Pose Estimation,” Proc. of SPIE Conf. on Mathematical Modeling and Estimation Techniques in Computer Vision, Vol. 3457, pp. 94-110, July 1998.
[13] I. Craw, D. Tock, and A. Bennett, “Finding Face Features,” Proceedings of the Second European Conference on Computer Vision, pp. 92-96, 1992.
[14] Y. Mitsukura, E. Fukumi, and N. Akamatsu, ”A Design of Face Detection System by Using Lip Detection Neural Network and Skin Distinction Neural Network,” IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, pp. 2789-2793, 2000.
[15] R.L. Hsu , M. Abdel-Mottaleb, and A.K. Jain,” Face Detection in Color Images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, Issue 5, pp. 696-706, May 2002.
[16] C.H. Morimoto and M. Flickner,” Real-time Multiple Face Detection Using Active Illumination,” Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Proceedings. pp. 8-13, 2000.
[17] 鍾國亮編著，影像處理與電腦視覺，第二版，東華書局，台北，2004。
[18] M. Petrou and P. Bosdogianni, “Image Enhancement,” in Image Processing: the fundamentals New York: John Wiley & Sons, pp. 125-154, 2001.
[19] M. Sonka, V. Hlavac, and R. Boyle, “Image Processing, Analysis, and Machine Vision,” PWS Pub. , New York, pp. 35-48, 1999.
[20] S. Abu-Bakerand and R.J. Green, “Detection of Edges Based on Hexagonal Pixel Formats,” IEEE Int’l Conf. on Signal Processing, Vol. 2, pp. 1114-1117, Oct. 1996.
[21] N. Kazakova, M. Margala, and N.G. Durdle, “Sobel Edge Detection Processor for a Real-Time Volume Rendering System,” Proceedings of the 2004 International Symposium on Circuits and Systems, Vol. 2, pp. 913-916, May 2004.
[22] J.P. Serra, “Image Analysis and Mathematical Morphology,” Acadwmic Press, pp. 115-130, 1982.
[23] S. Birchfield, “An Elliptical Head Tracker,” Conf. Record of the Asilomar Conf. on Signals, Systems & Computers, Vol. 2, pp. 1710-1714, Nov. 1997.
[24] S. Birchfield, “Elliptical Head Tracking Using Intensity Gradients and Color Histograms,” Proc. if the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 232-237, June 1998.
[25] R. Chellappa, C.L. Willson, and S. Sirohey, “Human and Mechine Recognition of Faces: A Survey,” Proceedings of the IEEE, Vol. 83, No. 5, pp. 705-740, 1995.
[26] P.J. Philips, H. Moon, P. Rauss, and S.A. Rizvi, “The FERET Evaluation Methodology for Face-Recognition Algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 10, pp. 1090-1104, 2000.
[27] M.A. Turk and A.P. Pentland, “Face Recognition Using Eigenfaces,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586-591, 1991.
[28] D. Swets and J. Weng, “Using Discriminant Eigenfeatures for Image Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8, pp. 831-836, 1996.
[29] J. Lu, K.N. Plataniotis, and A.N. Venetsanopoulos, “Face Recognition Using LDA-Based Algorithms,” IEEE Transactions on Neural Networks, Vol. 14, No. 1, pp. 195-200, 2003.
[30] D. Swets and J. Weng, “Using Discriminant Eigenfeatures for Image Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8, pp. 831-836, 1996.
[31] 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, No. 7, pp. 711-720, 1997.
[32] H. Yu and J. Yang, “A direct LDA algorithm for high-dimensional data — with application to face recognition,” Pattern Recognition, Vol. 34, No. 10, pp. 2067-2070, 2001.