The techniques for face recognition with support vector machines

The development of automatic visual control system is a very important research topic in computer vision. This face identification system must be robust to the various quality of the images such as light, face expression, glasses, beards, moustaches etc. We propose using the wavelet transformation algorithms for reduction the source data space. We have realized the method of the expansion of the values of pixels to the whole intensity range and the algorithm of the equalization of histogram to adjust image intensity values. The support vector machines (SVM) technology has been used for the face recognition in our work.

[1]  Gérard Dreyfus,et al.  Single-layer learning revisited: a stepwise procedure for building and training a neural network , 1989, NATO Neurocomputing.

[2]  Sunil Arya,et al.  An optimal algorithm for approximate nearest neighbor searching fixed dimensions , 1998, JACM.

[3]  Paul A. Viola,et al.  Boosting Image Retrieval , 2004, International Journal of Computer Vision.

[4]  Sungshin Kim,et al.  Real-time face detection and recognition using hybrid-information extracted from face space and facial features , 2005, Image Vis. Comput..

[5]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[6]  J. Platt Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[7]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[8]  John K. Tsotsos,et al.  Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..

[9]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[10]  Vladimir Vapnik,et al.  Universal learning technology : Support vector machines , 2005 .

[11]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[13]  Federico Girosi,et al.  An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.