Depth and Intensity Gabor Features Based 3D Face Recognition Using Symbolic LDA and AdaBoost

In this paper, the objective is to investigate what contributions depth and intensity informat ion make to the solution of face recognition problem when expression and pose variations are taken into account, and a novel system is proposed for combin ing depth and intensity information in order to improve face recognition performance. In the proposed approach, local features based on Gabor wavelets are extracted fro m depth and intensity images, wh ich are obtained fro m 3D data after fine align ment. Then a novel hierarch ical selecting scheme embedded in symbolic linear discriminant analysis (Symbolic LDA) with AdaBoost learning is proposed to select the most effective and robust features and to construct a strong classifier. Experiments are performed on the three datasets, namely, Texas 3D face database, Bhosphorus 3D face database and CASIA 3D face database, which contain face images with comp lex variations, including expressions, poses and longtime lapses between two scans. The experimental results demonstrate the enhanced effectiveness in the performance of the proposed method. Since most of the design processes are performed automatically, the proposed approach leads to a potential prototype design of an automat ic face recognition system based on the combination o f the depth and intensity informat ion in face images.