性別辨識在近年電腦視覺的領域中，是一個重要且有趣的課題，若能在日常中應用性別辨識，生活將變得更簡單和安全。譬如：在洗手間外，若性別辨識系統察覺到有異性徘徊，便能通知警衛處理以及對裡面的使用者發出警訊，避免意外發生。此外，此系統也可提供行人數量統計系統更詳細的統計資訊。
傳統的性別辨識，較多都是以輪廓特徵為基礎當方法來辨識男女，例如： Gait Energy Image(GEI)，但是GEI除了在側面90度的辨識率有不錯的表現外，若視覺角度改變之後，會造成辨識率大幅度的降低。因此本論文提出了一個以紋理特徵為基礎的方法－local binary patterns (LBP)，討論男女在衣著上或是身型上的差異，是否能用LBP展現。
將視訊影片中，每一張影像前景的LBP histograms取出並串連成向量，之後再使用support vector machine (SVM)分類器辨識性別，經實驗驗證後，發現以LBP histograms做為特徵時，除了使用單張影像就能辨識出性別外，視角變化對辨識率幾乎沒有影響(view-insensitive)，而且取得LBP特徵的速度及方法既快速又簡單。因此，對性別辨識來說，是個十分有效率的方法。

Recently, gender recognition is an important and interesting research issue in the area of pattern recognition. Its purpose is to recognize the gender of an unknown person which can be applied to ensure the secure activity in gender-restricted areas, such as lady’s room. Moreover, it can provide more detail statistical information for decision making in people counting application.
Most of traditional gender recognition methods use contour-based features, such as gait energy image (GEI), which perform well only in the view angle of 90 degree. To remove the restriction, we present a texture-based gender recognition method by using local binary patterns (LBP) in this thesis. The difference between the clothing and shapes of males and females can be successfully extracted and discriminated by LBP.
In our work, the LBP histograms are firstly extracted from the foreground of inputting video sequences and concatenate them into a single vector including the LBP histograms from the whole body, upper body without skin color, and lower body without skin color. The classifier that we adopt is support vector machine (SVM) in discriminating gender. Experimental results demonstrate that the proposed texture-based gender recognition method is more insensitive to view angles than GEI. The noticeable merit of our method is that we can classify human gender by using only one single image. Moreover, the extraction of LBP features needs much less time than the extraction of GEI features.

[1] Caifeng Shan, Shaogang Gong and Peter W. McOwan, “Fusing Gait and Face Cues for Human Gender Recognition”, Neurocomputing, vol. 71, pp. 1931-1938, 2008.
[2] Ju Han and Bir Bhanu, “Individual Recognition Using Gait Energy Image”, IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 28, No. 2, pp. 316-322, 2006.
[3] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns”, IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 24, pp. 971-987, 2002.
[4] K. Balci and V. Atalay, “PCA for Gender Estimation: Which Eigenvectors contribute?”, Proc. The 16th Intl. Conf. on Pattern Recognition, Vol. 3, pp. 363-366, 2002
[5] R. Iga, K. Izumi, H. Hayashi, “Gender and Age Estimation System from Face Images”, Proc. SICE Annual Conf., pp. 756-761, 2003.
[6] S. Hosoi, E. Takikawa, and M. Kawade, “Ethnicity Estimation with Facial Images”, Proc. Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 195-200, 2004
[7] H. C. Lian, B. L. Lu, E. Takikawa, and S. Hosoi, “Gender Recognition Using a Min-Max Modular Support Vector Machine”, Proc. ICNC 2005, LNCS 3611, pp. 438-441, 2005.
[8] N. P. Costen, M. Brown, and S. Akamatsu, “Sparse Models for Gender Classification”, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 201-206, 2004.
[9] T. Wilhelm, H. J. Bohme, and H. M. Gross, “Classification of Face Images for Gender, Age, Facial Expression, and Idendity”, Proc. International Conference on Artificial Neural Networks, pp. 569-574, 2005.
[10] Hui-Cheng Lian and Bao-Liang Lu, “Multi-view Gender Classification Using Multi-Resolution Local Binary Patterns and Support Vector Machines”, International Journal of Neural Systems, Vol. 17, No. 6, pp. 479-487, 2007.
[11] S. Yu, D. Tan, and T. Tan, “A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition”, International Conference on Pattern Recognition (ICPR), pp. 441-444, 2006.
[12] C. Garcia and G. Tziritas, “Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis”, IEEE Trans. On Multimedia, Vol. 1, No. 3, pp. 264-277, 1999.
[13] V. Vapnik, “Structure of Statistical Learning Theory”, Computational Learning and Probabilistic Reasoning, 1996.
[14] Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin, “A Practical Guide to Support Vector Classification”, National Taiwan University, Taipei, Taiwan, 2009.
[15] B. Moghaddam, and M. Yang, “Learning Gender with Support Faces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, pp. 707-711, 2002.
[16] G. Shakhnarovich, P.A. Viola, and B. Moghaddam, “A Unified Learning Framework for Real Time Face Detection and Classification”, IEEE International Conference on Automatic Face & Gesture Recognition, pp. 14-21, 2002.
[17] L. Lee, and W.E.L. Grimson, “Gait analysis for Recognition and Classification”, IEEE International Conference on Automatic Face and Gesture Recognition, 2002.
[18] S. Liao, X. Zhu, Z. Lei, L. Zhang, and S. Z. Li, “Learning Multi-scale Block Local Binary Patterns for Face Recognition”, ICB 2007, LNCS 4642, pp. 828-837, 2007.