Face Detection Using HMM –SVM Method

This paper proposes a method for face detection and recognition using Modified Hidden Markov Model (HMM) and Support Vector Machine (SVM). It is a two layer architecture system that identifies all image regions which contain face or non-face. At the first stage, the Kernel HMM classifies input pattern into three classes: a face class, undecided class or non-face class. In the final stage, SVM detects the face class or non-face class if any sub-image falsely judged as undecided class. This system alleviates the problem of false positive rate. The experimental result shows that the proposed approach outperforms some of the existing face detection methods and we have compared various face detection method.

[1]  Vennila Ramalingam,et al.  Facial expression recognition - A real time approach , 2009, Expert Syst. Appl..

[2]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[3]  Tomaso A. Poggio,et al.  Face recognition: component-based versus global approaches , 2003, Comput. Vis. Image Underst..

[4]  K Karibasappa,et al.  Face detection using modified FDA-SVM method , 2009 .

[5]  Shaogang Gong,et al.  Composite support vector machines for detection of faces across views and pose estimation , 2002, Image Vis. Comput..

[6]  Wang Zhong-dong,et al.  New approach to training support vector machine , 2006 .

[7]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[8]  Chengjun Liu,et al.  Face detection using discriminating feature analysis and Support Vector Machine , 2006, Pattern Recognit..

[9]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[10]  Monson H. Hayes,et al.  Hidden Markov models for face recognition , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[11]  Steve J. Young,et al.  HMM-based architecture for face identification , 1994, Image Vis. Comput..

[12]  Masakazu Suzuki,et al.  Mathematical symbol recognition with support vector machines , 2007, Pattern Recognit. Lett..

[13]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.