An Efficient Method for Face Recognition Using Kernel Discriminant Analysis

The kernel approach has been proposed to solve face recognition problems under complex distribution by mapping the input space to a high-dimensional feature space. The experimental results show that the kernel-based method is a good and feasible approach to tackle the pose and illumination variations. One of the crucial factors in the kernel approach is the selection of kernel parameters, which highly affects the generalization capability and stability of the kernel-based learning methods. 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. 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.