Weighted Gradient Feature Extraction Based on Multiscale Sub-Blocks for 3D Facial Recognition in Bimodal Images

Abstract

In this paper, we propose a bimodal 3D facial recognition method aimed at increasing the recognition rate and reducing the effect of illumination, pose, expression, ages, and occlusion on facial recognition. There are two features extracted from the multiscale sub-blocks in both the 3D mode depth map and 2D mode intensity map, which are the local gradient pattern (LGP) feature and the weighted histogram of gradient orientation (WHGO) feature. LGP and WHGO features are cascaded to form the 3D facial feature vector LGP-WHGO, and are further trained and identified by the support vector machine (SVM). Experiments on the CASIA database, FRGC v2.0 database, and Bosphorus database show that, the proposed method can efficiently extract the structure information and texture information of the facial image, and have a robustness to illumination, expression, occlusion and pose.