Abstract A Real AdaBoost algorithm based EOM (edgeorientation matching) method is proposed for face detection. The edge orientation feature is extracted from the original face images to eliminate the influence of some disturbances such as variable lighting to a certain extent. The global face pattern (global feature point set) is obtained by using the Real AdaBoost algorithm through multiple iterative learning procedures, and the local pattern (local feature point set) is acquired by utilizing the areaselecting strategy during each iterative procedure. A precise face pattern is found by the proposed method rather than by the original EOM method, which is confirmed by the experiment of frontal face detection.
[1] Yang M H, Kriegman D J, Ahuja N. Detecting Faces in Images: A Survey. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(1): 34-58 [2] Liang Luhong, Ai Haizhou, Xu Guangyou, et al. A Survey of Human Face Detection. Chinese Journal Computers, 2002, 25(5): 449-458 (in Chinese) (梁路宏,艾海舟,徐光佑,等.人脸检测研究综述.计算机学报, 2002, 25(5): 449-458) [3] Viola P, Jones M J. Rapid Object Detection Using a Boosted Cascade of Simple Features // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Hawaii, USA, 2001, Ⅰ: 511-518 [4] Zhang Z Q, Zhu L Z, Li S, et al. Real-Time Multi-View Face Detection // Proc of the 5th International Conference on Automatic Face and Gesture Recognition. Washington, USA, 2002: 142-147 [5] Frba B, Küblbeck C. Real-Time Face Detection Using Edge-Orientation Matching // Proc of the 3th International Conference on Audio-and Video-Based Biometric Person Authentication. Halmstad, Sweden, 2001: 78-83 [6] Frba B, Ernst A. Fast Frontal-View Face Detection Using a Multi-Path Decision Tree // Proc of the 4th International Conference on Audio-and Video-Based Biometric Person Authentication. Heidelberg, Germany, 2003: 921-928 [7] Schapire R E. A Brief Introduction to Boosting // Proc of the 16th International Joint Conference on Artificial Intelligence. Stockholm, Sweden, 1999: 1401-1406 [8] Schapire R E, Freund Y, Bartlett P, et al. Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. Annals of Statistics, 1998, 26(5): 1651-1686 [9] Schapire R E, Singer Y. Improved Boosting Algorithms Using Confidence-Rated Predictions. Machine Learning, 1999, 37(3): 297-336 [10] Friedman J, Hastie T, Tibshirani R. Additive Logistic Regression: A Statistical View of Boosting. Annals of Statistics, 2000, 28(2): 337-407 [11] Sung K K, Poggio T. Example-Based Learning for View-Based Human Face Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1998, 20(1): 39-51 [12] Jesorsky O, Kirchberg K, Frischholz R. Robust Face Detection Using the Hausdorff Distance // Proc of the 3rd International Conference on Audio-and Video-Based Biometric Person Authentication. Halmstad, Sweden, 2001: 90-95 [13] Bian Zhaoqi, Zhang Xuegong, et al. Pattern Recognition. 2nd Edtion. Beijing, China: Tsinghua University Press, 2000 (in Chinese) (边肇祺,张学工,等.模式识别. 第2版. 北京: 清华大学出版社, 2000) [14] Rowley H A, Baluja S, Kanade T. Neural Network-Based Face Detection. IEEE Trans on Pattern Analysis and Machine Intelligence, 1998, 20(1): 23-38 [15] Osuna E, Freund R, Girosi F. Training Support Vector Machines: An Application to Face Detection // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Juan, Puerto Rico, 1997: 130-136 [16] Roth D, Yang M H, Ahuja N. A SNoW-Based Face Detector // Solla S A, Leen T K, Müller K R, eds. Advances in Neural Information Processing Systems. Cambridge, USA: MIT Press, 2000: 855-861 [17] Schneiderman H, Kanade T. Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Santa Barbara, USA, 1998: 45-51 [18] Nefian A V, Hayes M H. Hidden Markov Models for Face Detection and Recognition // Proc of the IEEE International Conference on Image Processing. Seattle, USA, 1998, Ⅰ: 141-145
2006, Vol. 19 
