人臉辨識為樣形識別發展的一個重要應用，目前，人臉辨識技術已廣泛的運用在我們日常生活中，例如:機場的快速通關系統，門禁系統等等。
人臉辨識系統容易受到角度、光照與表情變化的影響而降低系統辨識率，所以如何處理這些問題，提高辨識率，即是人臉辨識技術的重要挑戰。
人臉辨識主要分為人臉偵測、影像前處理、特徵抽取與特徵比對四個步驟，梯度方向直方圖(Histogram of oriented Gradients，簡稱HOGs)特徵能夠優異地擷取人臉的邊緣與輪廓資訊，與對光照影響具有良好的抵抗性，故我們採用HOG為人臉辨識系統的特徵。單純使用HOG特徵時，若人臉圖片發生旋轉偏移，則會導致辨識率下降，故本論文在影像前處理時加入旋轉偏移校正的功能，以改善人臉角度旋轉對系統辨識率的影響。
本論文使用兩個人臉資料庫進行實驗分析，分別是PIE與FETET人臉資料庫，在FERET人臉資料庫的實驗將與Albiol et al.論文[15]進行比較，探討HOG特徵中各個參數對辨識率的影響；而在其他實驗也將探討人臉旋轉角度、光照與表情變化對系統辨識率FAR與FRR的影響。
最後的實驗結果顯示，我們所提出的前處理方法能夠有效地改善使用HOG特徵時的系統辨識率。

Face recognition is an important application in the field of pattern recognition. Face recognition technology is commonly used in our daily life, such as e-Gate, access control system, etc. The recognition rate will drop considerably when the head pose or illumination variation is too large, or when there is expression on the face. The greatest challenge is how to get over these difficulties.
Face recognition can be divided into the following four steps: face detection, image preprocessing, feature extraction, and feature matching. Histogram of oriented Gradients (HOGs) feature is an effective descriptor for the contour of the face, and robust to illumination effect. So we choose HOG to be the feature in our system. In order to compensate for errors in facial recognition due to rotation changes, we proposed a rotation detection and correction method based on eye-location.
Two human face databases, namely FERET database and CMU PIE, are used in our experiments to show the recognition performance of our proposed method and system. In the FERET database experiment, it is found that the rotation degrades the recognition rate drastically, using only HOG feature and subjecting to illumination and facial expression variations. But with the proposed rotation correction preprocessing, the recognition rate of our method is improved over that of the paper [15]. This improvement is also observed in the experiments on CMU PIE database where both EER and FRR/FAR are improved with the proposed rotation correction preprocessing and HOG features under illumination and facial expression variations. Finally, a PC/NB-webcam based face recognition system using the proposed rotation correction preprocessing and HOG features and the combined FERET and PIE face databases is implemented. Some experiments show the rotation and illumination resilient property of our proposed system.

[1] A. K. Jain, "Technology: Biometric Recognition," Nature, vol. 449, pp. 38-40, Sep 6 2007.
[2] W. W. Bledsoe, “Man-machine Facial Recognition,” Tech. Rep. PRI: 22, Panoramic Res. Inc., Palo Alto, CA, 1966.
[3] T. Kanade, "Picture Processing System by Computer Complex and Recognition of Human Faces," Doctoral Dissertation, Kyoto University, 1973.
[4] R. Brunelli and T. Poggio, "Face Recognition: Features Versus Templates," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 15, pp. 1042-1052, 1993.
[5] M. Turk and A. Pentland, "Eigenfaces for Recognition," J Cogn Neurosci, vol. 3, pp. 71-86, Winter 1991.
[6] V. P. Kshirsagar, M. R. Baviskar, and M. E. Gaikwad, "Face Recognition Using Eigenfaces," in Computer Research and Development (ICCRD), 2011 3rd International Conference on, 2011, pp. 302-306.
[7] P. N. Belhumeur, J. P. Hespanha, and D. Kriegman, "Eigenfaces vs. Fisherfaces: recognition using class specific linear projection," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 19, pp. 711-720, 1997.
[8] M. Bartlett and T. Sejnowski, Independent components of face images: A Representation for Face Recognition. In Proc. the 4th Annual Joint Symposium on Neural Computation, Pasadena, CA, May 17, 1997.
[9] D. Gabor, "Theory of Communication. Part 1: The Analysis of Information," Electrical Engineers - Part III: Radio and Communication Engineering, Journal of the Institution of, vol. 93, pp. 429-441, 1946.
[10] J. G. Daugman, "Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-dimensional Visual Cortical Filters," J Opt Soc Am A, vol. 2, pp. 1160-9, Jul 1985.
[11] L. Wiskott, J. M. Fellous, N. Kuiger, and C. von der Malsburg, "Face recognition by elastic bunch graph matching," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 19, pp. 775-779, 1997.
[12] G. Zhenhua, D. Zhang, and D. Zhang, "A Completed Modeling of Local Binary Pattern Operator for Texture Classification," Image Processing, IEEE Transactions on, vol. 19, pp. 1657-1663, 2010.
[13] T. Ahonen, A. Hadid, and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Trans Pattern Anal Mach Intell, vol. 28, pp. 2037-41, Dec 2006.
[14] N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, pp. 886-893 vol. 1.
[15] A. Albiol, D. Monzo, A. Martin, J. Sastre, and A. Albiol, "Face Recognition Using HOG–EBGM," Pattern Recognition Letters, vol. 29, pp. 1537–1543, 2008.
[16] O. Déniz, G. Buenoa, J. Salidoa, F. De la Torre,” Face Recognition Using Histograms of Oriented Gradients.”, Pattern Recognition Letters, vol. 32, pp. 1598–1603, September 2011.
[17] A. K. Jaina, Y. Zhongb, and M.-P. Dubuisson-Jollyc, "Deformable Template Models: A Review," Signal Processing, vol. 71, pp. 109–129, 1998.
[18] H. Rein-Lien, M. Abdel-Mottaleb, and A. K. Jain, "Face Detection in Color Images," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, pp. 696-706, 2002.
[19] H. A. Rowley, S. Baluja, and T. Kanade, "Neural Network-based Face Detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 20, pp. 23-38, 1998.
[20] H. A. Rowley, S. Baluja, and T. Kanade, "Rotation Invariant Neural Network-based Face Detection," in Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on, 1998, pp. 38-44.
[21] S. Kah-Kay and T. Poggio, "Example-based Learning for View-based Human Face Detection," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 20, pp. 39-51, 1998.
[22] P. Viola and M. Jones, "Robust Real-time Object Detection," International Journal of Computer Vision, vol. 4, pp. 34-47, 2001.
[23] 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, pp. 119-139, 1997.
[24] R. E. Schapire and Y. Singer, Improved Boosting Algorithms Using Confidence-rated Predictions, 1999.
[25] T. Sim, S. Baker, and M. Bsat, "The CMU pose, illumination, and expression database," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1615-1618, 2003.
[26] P. J. Phillips, P. J. Rauss, and S. Z. Der, " FERET (Face Recognition Technology) Recognition Algorithm Development and Test Results", October 1996. Army Research Lab technical report 995.
[27] P. J. Phillips, H. Moon, P. J. Rauss, and S. Rizvi, "The FERET Evaluation Methodology for Face Recognition Algorithms", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 10, October 2000.
[28] K.-W. Lin, "Head-Shoulder Detection Using HOG-based AdaBoost Approach," master's thesis, National Tsing Hua University, 2011.
[29]http://www.itl.nist.gov/iad/humanid/feret/feret_master.html