人臉偵測與性別辨識都是近年來熱門的研究課題，可應用於許多層面，如電子廣告、監視系統等等。本篇論文中開發出一套新的平面旋轉人臉偵測系統，是基於局部組合二位元 (Locally Assembled Binary, LAB) 特徵和串聯階層式AdaBoost演算法訓練出一個正向人臉偵測器，接著在測試過程裡透過旋轉特徵的方式，建立出各種角度的人臉偵測器來進行偵測。依照不同偵測需求，局部組合二位元特徵可以使用不同的編碼方式來擁有不同的偵測特性，藉此提高偵測效果。經實驗數據顯示我們所提出的系統比起其他相關的技術所花費的時間較少，而且能兼顧偵測準確率。在性別辨識方面，本篇論文使用概率串聯增強樹 (Probabilistic Boosting Tree, PBT) 改良現有Cascade演算法來訓練和測試男女影像，經實驗證明顯示，我們的性別辨識系統不僅能提供較高準確率，而且可以擁有較平均的男性和女性分類正確率。

Face detection and gender identification are hot research topic in recent years, both could be applied to many categories, e.q. electronic advertising, surveillance systems, etc. In this thesis we propose a novel framework for roll-rotation face detection. Based on Locally Assembled Binary feature and Cacaded AdaBoost algorithms to train a forward face detector. In the testing process, different angles of the face detectors are created by rotating feature, and use these detectors to detect testing image. According to the needs of different detection, Locally Assembled Binary feature can use different encoding methods to have different characteristics and improve the detection results. The experimental results demonstrate that our proposed system takes less time than other related technologies while maintaining a good accuracy rate. In gender classification case, this thesis use Probabilistic Boosting Tree to improve Cascade algorithm for training and testing. The experimental results demonstrate that our proposed system not only provides a higher accuracy rate, but also with an average rate of correct classification.

[1] Y. Abramson and B. Steux. “YEF* Real-Time Object Detection,” in Automatic Learning and Real-Time, pp. 5-13, 2005.
[2] T. Ahonen, A. Hadid, and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no.12, pp.2037-2041, 2006.
[3] S. Baluja, M. Sahami, and H. A. Rowley, “Efficient Face Orientation Discrimination,” in International Conference on Image Processing, 2004.
[4] S. Baluja and H. Rowley, “Boosting Sex Identification Performance,” in Internatinal Journal of Computer Vision, vol. 71, no. 1, pp. 111-119, 2007.
[5] C. Ben-Abdelkader and P. Griffin, “A Local Region-Based Approach to Gender Classification from Face Images,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 52-52, 2005.
[6] R. Brunelli and T. Poggio, “Hyperbf Networks for Gender Classification,” in Proceedings of the DARPA Image Understanding Workshop, pp. 311-314, 1992.
[7] N. Dalal and B. Triggs, “Histogram of Oriented Gradients for Human Detection,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.
[8] B. Fröba and A. Ernst, “Face Detection with the Modified Census Transform,” in IEEE Conference on Face and Gesture Recognition, pp. 91-96, 2004.
[9] B. Fröba and A. Ernst, ”Fast Frontal-View Face Detection Using a Multi-Path Decision Tree,” in Audio- and Video-based Biometric Person Authentication, pp. 921-928, 2003.
[10] W. Gao and H. Ai. “Face Gender Classification on Consumer Images in A Multi-Ethnic Environment,” in International Conference on Biometrics, 2009.
[11] Golomb et. al, “SEXNET: a Neural Network Identifies Sex from Human Faces,” in Advances in Neural Information Processing Systems, pp. 572-577, 1991.
[12] C. Huang, H. Ai, Y. Li, and S. Lao, “Vector Boosting for Rotation Invariant Multi-View Face Detection,” in International Conference on Computer Vision, pp.446-453, 2005.
[13] C. Huang, H. Ai, Y. Li, and S. Lao, “Learning Sparse Features in Granular Space for Multi-View Face Detection,” in Automatic Face and Gesture Recognition, pp.401-406, 2006.
[14] T. Jabid, M. Hasanul Kabir, and Oksam Chae, “Gender Classification Using Local Directional Pattern (LDP),” in International Conference on Pattern Recognition, p.2162-2165, 2010.
[15] M. Jones and P. Viola, “Fast Multi-View Face Detection,” in TR2003-96, 2003.
[16] B.Kisacanin, "Integral Image Optimizations for Embedded Vision Applications," in Proc IEEE SSIAI, pp181-184, 2008.
[17] A. L. C. Barczak, M. J. Johnson, and C. H. Messom, "Real-Time Computation of Haar-like Features at Generic Angles for Detection Algorithms," in Research Letters in the Information and Mathematical Sciences, pp.98–111, 2006.
[18] K. Levi and Y. Weiss, “Learning Object Detection from a Small Number of Examples: The Importance of Good Features,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 53–60, 2004.
[19] H.-C. Lian and B.-L. Lu, “Multi-View Gender Classification Using Local Binary Patterns and Support Vector Machines,” in Third International Symposium on Neural Networks (ISNN), vol. 2, pp. 202-209, 2006.
[20] Y. Li, S. Gong, J. Sherrah, and H. Liddell, “Support Vector Machine Based Multi-View Face Detection and Recognition,” in Image and Vision Computing, vol. 22, no. 5, pp. 413-427, 2004.
[21] R. Lienhart and J. Maydt, “An Extended Set of Haar-Like Features for Rapid Object Detection,” In Proc. of International Conference on Image Processing, pp. 900-903, 2002.
[22] H. Lu, Y. Huang, and Y. Chen, "Automatic Gender Recognition Based on Pixel-Pattern-Based Texture Feature," In J Real-Time Proc., pp. 109-116, 2008.
[23] E. Makinen and R. Raisamo, “An Experimental Comparison of Gender Classification Methods,” in Pattern Recognition Letters, vol. 29, pp. 1544-1556, 2008.
[24] T. Mita, T. Kaneko, and O. Hori, “Joint Haar-Like Features for Face Detection,” in Proc. of International Conference on Computer Vision, pp.1619-162 , 2005.
[25] B. Moghaddam, M. Yang, “Gender Classification with Support Vector Machines,” in Proceedings of International Conference on Automatic Face and Gesture Recognition, pp. 306-313, 2000.
[26] T. Ojala, M. Pietikäinen, and D. Harwood, "Performance Evaluation of Texture Measures with Classification Based on Kullback Discrimination of Distributions", in International Conference on Pattern Recognition, vol. 1, pp. 582-585, 1994
[27] H. Rowley, S. Baluja, and T. Kanade. “Neural Network-Based Face Detection,” in IEEE Patt. Anal. Mach. Intell., vol. 20, pp. 22–38, 1998.
[28] Chin, T., Goh, H., and Tan, N.M. "Exact Integral Images at Generic Angles for 2D Barcode Detection," in International Conference on Pattern Recognition, pp. 1-4, 2008.
[29] S. Tamura, H. Kawa, and H. Mitsumoto, “Male/Female Identification from 8x6 Very Low Resolution Face Images by Neural Network,” in Pattern Recognition, vol. 29, pp. 331-335, 1996.
[30] J. Trefný and J. Matas, “Extended Set of Local Binary Patterns for Rapid Object Detection”, in Computer Vision Winter Workshop, 2010.
[31] Z. Tu, “Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering”, in Proc. of International Conference on Computer Vision, pp. 1589-1596, 2005.
[32] R. Verschae, J. Ruiz-del-Solar, and M. Correa, “Gender Classification of Faces Using Adaboost,” Lecture Notes in Computer Science, vol. 4225, pp. 68-78, 2006.
[33] P. Viola and M Jones, "Fast and Robust Classification Using Asymmetric AdaBoost and a Detector Cascade", in Neural Information Processing Systems, pp.1311-1318, 2001.
[34] P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, 2001.
[35] P. Viola and M Jones, "Robust Real-time Object Detection", in International Journal of Computer Vision, vol. 57, no. 2, pp.137-154, 2004
[36] P.Wang and Q. Ji, “Multi-View Face Detection Under Complex Scene Based on Combined Svms,” in Proc. of International Conference on Pattern Recognition, vol. 4, pp. 179-182, 2004.
[37] B. Wu, H. Ai, and C. Huang, “Lut-Based AdaBoost for Gender Classification,” in Audio- and Video-Based Biometric Person Authentication, pp. 104–110, 2003.
[38] B. Wu, H. Ai, and C. Huang, “Real-Time Gender Classification,” in Third International Symposium on Multispectral Image Processing and Pattern Recognition, vol. 5286, pp. 498-503, 2003.
[39] B. Wu, H. Ai, C. Huang, and S. Lao, “Fast Rotation Invariant Multi-View Face Detection Based on Real AdaBoost,” in Proc. of IEEE Conf. on. Automatic Face and Gesture Recognition, pp. 79 - 84, 2004.
[40] J. Wu, C. Brubaker, "Fast Asymmetric Learning for Cascade Face Detection", in IEEE transaction on Pattern Analysis and Machine Intelligence, pp.369-382, 2008.
[41] X. Wang, T. X. Han, and S. Yan, ”An HOG-LBP Human Detector with Partial Occlusion Handling,” in Proc. of International Conference on Computer Vision, pp. 32-39, 2009.
[42] S. Yan, S. Shan, X. Chen, and W. Gao, “Locally Assembled Binary (LAB) Feature with Feature-Centric Cascade for Fast and Accurate Face Detection,” in Proc. of Computer Vision and Pattern Recognition, pp. 1-7, 2008.
[43] Y.-Y. Lin and T.-L. Liu, "Robust Face Detection with Multiclass Boosting," in Proc. of Computer Vision and Pattern Recognition, pp.680-687, 2005.
[44] Tu, Z., Zhou, X., Barbu, A., Bogoni, L., and Comaniciu, D, "Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment," in Proc. of Computer Vision and Pattern Recognition, pp. 1544~1551, 2006.
[45] Lun Zhang, Rufeng Chu, Shiming Xiang, ShengCai Liao, and Stan Z. Li, “Face Detection Based on Multi-Block Lbp Representation,” in Independent Commission on Banking, pp. 11–18, 2007.
[46] C. Zhang and Z. Zhang, “A Survey of Recent Advances in Face Detection,” in Microsoft Research Technical Report, 2010.