FACE RECOGNITION BASED ON KERNEL RADIAL BASIS FUNCTION NETWORKS

Linear subspace analysis has been extensively applied to face recognition. However, a linear subspace can not describe the nonlinear variations of face images. Alternatively, a kernel feature space can reflect nonlinear information of faces. In this paper we present a new face recognition method based on Radial Basis Function (RBF) networks in kernel space. The face features are extracted in kernel space and then fed into an RBF network, resulting in a face recognition algorithm that is computationally simple and robust. The experimental results show that our algorithm performs better than a traditional RBF network for face recognition.

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