Local Gabor Binary Patterns Based on Mutual Information for Face Recognition

Appropriate representation is one of the keys to the success of face recognition technologies. In this paper, we present a novel face representation approach based on a reduced 21 set of local histograms based on Local Gabor Binary Patterns (LGBP). In the proposed method, a face image is first represented by the local Gabor binary patterns (LGBP) his23 tograms which are extracted from the LGBP images. Then, the local LGBP histograms with high separability and low relevance are selected to obtain a dimension-reduced face 25 descriptor. Extensive experimental results demonstrate that the proposed method not only greatly reduces the dimensionality of face representation, but also outperforms the 27 state-of-the-art approaches for face recognition, such as Fisherfaces, and Gabor Fisher Classification (GFC). 29