Three-dimensional face recognition under expression variation

In this paper, we introduce a fully automatic framework for 3D face recognition under expression variation. For 3D data preprocessing, an improved nose detection method is presented. The small pose is corrected at the same time. A new facial expression processing method which is based on sparse representation is proposed subsequently. As a result, this framework enhances the recognition rate because facial expression is the biggest obstacle for 3D face recognition. Then, the facial representation, which is based on the dual-tree complex wavelet transform (DT-CWT), is extracted from depth images. It contains the facial information and six subregions' information. Recognition is achieved by linear discriminant analysis (LDA) and nearest neighbor classifier. We have performed different experiments on the Face Recognition Grand Challenge database and Bosphorus database. It achieves the verification rate of 98.86% on the all vs. all experiment at 0.1% false acceptance rate (FAR) in the Face Recognition Grand Challenge (FRGC) and 95.03% verification rate on nearly frontal faces with expression changes and occlusions in the Bosphorus database.