Abstract
The anisotropic diffusion (AD) algorithm is well known for the illumination invariant feature extraction of face images. The performance of anisotropic diffusion algorithm depends on its conduction function and discontinuity measure. However, in the traditional anisotropic diffusion algorithm, the discontinuity measure usually adopts space gradient or in-homogeneity. Though they are good at describing the local variation of image well, they cannot reflect the variation relative to its background. The relative variations tend to be more able to reflect the variation's degree of local image. In this paper, we propose an improved anisotropic diffusion algorithm that uses Weber local descriptor (WLD), a powerful and robust local descriptor as the discontinuity measure. Then, we introduce a centre-symmetric logarithmic transformation to eliminate the effect of shadow boundary. Experiments are executed in our proposed illumination invariant face verification scheme on CMU PIE, CAS-PEAL databases and a self-built real-life face database. The results demonstrate that the proposed method outperforms some typical methods on the face databases with large illumination variations.
Published Version
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