Discretized Gabor Statistical Models for Face Recognition
Aiming to the use of texture information of Gabor filtered face images, the authors present a new method matching multi-channel Gabor marginal statistical models for face recognition. Given partitions on channel Gabor magnitude spaces, the ensemble of magnitude sets of Gaborfaces is modeled as a probabilistic realization of a set of multinomial models. The histogram method is adopted to obtain corresponding empirical models for algorithmic implementation. With the Fisher geometry on multinomial family, the Fisher information distance is extended to the closure of each channel model space for quantifying information divergence between factorial histograms in a natural product framework.