Robust Ldp Based Face Descriptor

This paper presents a novel LDP based image descriptor which is more robust to temporal face changes. LDP is a framework to encode directional pattern based on local derivative variations, hence LDP is highly directional. However texture based features extracted globally tend to average over the image area. Hence this paper proposes to divide the face image into multiple regions and perform LDP separately on each region. The procedure consists of using the texture descriptor to build several local descriptions of the face and combining them into a global description. Block based processing ensures robustness against pose, facial expressions and other temporal face changes. Also, a modification in Chi Square Distance is proposed to suit the spatial histogram matching of faces. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, and Extended Yale B databases show that the LDP based face descriptors consistently performs better than LDP extracted globally for both face identification and face verification under various conditions.