Kernel subspace LDA with optimized kernel parameters on face recognition

This work 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 LDA-based methods with kernel parameter selected by experiment manually, the results are encouraging.