A fast and robust algorithm for face detection and localization

This paper presents a fast and robust face detection and localization approach. The major techniques employed in method are the support vector machine (SVM) and the generalized symmetric map. The proposed face detection system consists of 3 stages. In the first stage, a feature detection technique is used to hypothesize candidate regions by looking for gross features. The facial features of the concern are the pair of eyes. By making use of the symmetrical property of eyes, a generalized symmetry approach is used. The exhaustive pairing of eyes gives the positions of potential face candidates. In the second stage, a view-based approach is used to verify the face candidates obtained in the first stage. A SVM is trained to do the face candidate verification. The last stage of the system precisely locates the eye positions. The proposed detection system was evaluated using the Bern face database. A detection rate of 96.67% was obtained.

[1]  C. Taylor,et al.  Active shape models - 'Smart Snakes'. , 1992 .

[2]  Yehezkel Yeshurun,et al.  Robust detection of facial features by generalized symmetry , 1992, [1992] Proceedings. 11th IAPR International Conference on Pattern Recognition.

[3]  Tomaso A. Poggio,et al.  Pedestrian detection using wavelet templates , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Ashok Samal,et al.  Automatic recognition and analysis of human faces and facial expressions: a survey , 1992, Pattern Recognit..

[5]  Federico Girosi,et al.  Support Vector Machines: Training and Applications , 1997 .

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

[9]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.