A new model for Gabor coefficients' magnitude in face recognition
Gabor filters have demonstrated their effectiveness in automatic face recognition, which can greatly benefit from an accurate statistical model for Gabor-based face representations. Previous approaches have modeled real and imaginary parts independently as Generalized Gaussians (GG). Since most Gabor-based face recognition systems discard coefficients' phase, we propose a novel statistical model for the magnitude of Gabor coefficients that accounts for the dependence between real and imaginary parts, assuming they are circularly symmetric and marginally GG distributed. The quality of the fit for our model is assessed using the Kullback-Leibler divergence, and optimal quantization of Gabor coefficients is shown as one of its applications.