Abstract
A novel face recognition algorithm based on Gabor texture information is proposed in this paper. Two kinds of strategies to capture it are introduced: Gabor magnitude-based texture representation (GMTR) and Gabor phase-based texture representation (GPTR). Specifically, GMTR is characterized by using the Gamma density ( Γ D) to model the Gabor magnitude distribution, while GPTR is characterized by using the generalized Gaussian density (GGD) to model the Gabor phase distribution. The estimated model parameters serve as texture representation. Experiments are performed on Yale, ORL and FERET databases to validate the feasibility of the proposed method. The results show that the proposed GMTR-based and GPTR-based NLDA both significantly outperform the widely used Gabor features-based NLDA and other existing subspace methods. In addition, the feature level fusion of these two kinds of texture representations performs better than them individually.
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