Gabor feature-based face recognition on product gamma manifold via region weighting

In this paper, we put forward a novel product statistical manifold framework for face recognition based on the use of Gabor features. The collection of multi-region multi-channel Gabor magnitude sets is characterized as a point on a product Gamma manifold by generative modeling and maximum likelihood estimation (MLE)-based product embedding. Although intrinsic analysis on statistical manifolds seems to be a conventional approach, the development of computational information geometry involving intrinsic tools is still somewhat lagging. For this reason, we focus on extrinsic tools and introduce an immersion of product Gamma manifolds to facilitate incorporating the method of dual-space linear discrimnant analysis (DLDA) into our recognition system. With the learned region-adaptive distance metrics and weights, we can integrate regional discriminative information in product magnitude-generating model matching. Experimental results on FERET and CMU-PIE databases show that the performance of the proposed method is competitive.