A pair-wise decision fusion framework: recognition of human faces

Automatic detection and recognition offaces is now becoming an important tool in securiQ and suweillance. One of the most promising techniques for recognizing faces is the use of Support Vector Machines (SVMs). However, SVMs are essentially tw-class classifiers or dichotomizers, and applying them IO recognize multiple classes of faces requires post- recogni:ion decision fusion to arrive at the final overall decision. In this paper, we propose such a decision fusion framewrk, and show chat such a pair-nise classification framework can be totally generic irrespective of the chosen classifier. Application o/ this framework to the face recognition problem has resulted in encouraging perl/ormance.

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