Multi-modal face recognition based on few feature points

Aimed at solving the problem that 2D face recognition is sensitive to pose and illumination variations, a multi-modal face recognition approach, which is based on few feature points, is proposed. In the training stage, in order to set up the complete feature template, 3D face data are reprocessed and exploited; so as to overcome the nonlinear problem of feature extraction, a simple and effective clustering algorithm is designed, subsequently; Local Feature Analysis(LFA)is implemented to extract features of few feature points, and fusing local and global features. Experiment results confirm that the novel approach is not only efficient, but also robust to pose and illumination variations, and achieves 98.06% recognition rate on WHU-3D small-scale face database.