Kernel subspace LDA with optimized kernel parameters on face recognition

Authors:
Jian Huang;Pong C. Yuen;Wen-Sheng Chen;J. H. Lai
Affiliations:
Department of Computer Science, Hong Kong Baptist University;Department of Computer Science, Hong Kong Baptist University;Department of Computer Science, Hong Kong Baptist University and Department of Mathematics, Shenzhen University, P.R.China;Department of Mathematics, ZhongShan University, P.R.China
Venue:
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Year:
2004

Citing 6
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Abstract

This paper addresses the problem of selection of Kernel parameters in Kernel Fisher Discriminant for face recognition. We propose a new criterion and derive a new formation in optimizing the parameters in RBF kernel based on the gradient descent algorithm. The proposed formulation is further integrated into a subspace LDA algorithm and a new face recognition algorithm is developed. FERET database is used for evaluation. Comparing with the existing Kernel LDAbased methods with kernel parameter selected by experiment manually, the results are encouraging.