Weighted Extreme Sparse Classifier and Local Derivative Pattern for 3D Face Recognition

摘要

A novel weighted hybrid classifier and a high-order, local normal derivative pattern descriptor are proposed for 3D face recognition. The local derivative pattern (LDP) captures the detailed information based on the local derivative variation in different directions. The LDP is computed on three normal maps in x-, y-, and z-directions and on different scales. The surface normal captures the orientation of a surface at each point of 3D data. More informative local shape information is extracted using the surface normal, as compared to depth. The nth-order LDP on the surface normal is proposed to encode the more detailed features from the (n-1)th-order's local derivative direction variations. An extreme learning machine (ELM)-based autoencoder, using a multilayer network structure, is employed to select more discriminant features and to provide a faster training speed. A weighted hybrid framework is proposed to handle facial challenges using a combination of the ELM and the sparse representation classifier (SRC). The advantage of speed for the ELM and the accuracy for the SRC in a weighted scheme is used to enhance the performance of the recognition system. Experimental results regarding four famous 3D face databases illustrate the generalization and effectiveness of the proposed method in terms of both computational cost and recognition accuracy.