Local Gaussian Directional Pattern for face recognition

We propose a novel local feature descriptor, Local Gaussian Directional Pattern (LGDP), for face recognition. LGDP encodes the directional information of the face's textures (i.e., the texture's structure) in a compact way, producing a more discriminating code than other methods. The structure of each micro-pattern is computed by using a derivative-Gaussian compass mask, and encoded by using its prominent directions and sign — which allows it to distinguish among similar structural patterns that have different intensity transitions. Moreover, our descriptor extracts several facial characteristics by varying the size of its mask, to recover features that may be missed in just one resolution. We construct the face descriptor by concatenating the LGDP's distributions extracted from a uniform grid of the face. We perform several experiments in which our descriptor performs consistently under illumination, noise, expression and age variations.