Multiscale depth local derivative pattern for sparse representation based 3D face recognition

摘要

3D face recognition is a popular research area due to its vast application in biometrics and security. Local feature-based methods gain importance in the recent years due to their robustness under degradation conditions. In this paper, a novel high-order local pattern descriptor in combination with sparse representation based classifier (SRC) is proposed for expression robust 3D face recognition. 3D point clouds are converted to depth maps after preprocessing. Multi-directional derivatives are applied in spatial space to encode the depth maps based on the local derivative pattern (LDP) scheme. Directional pattern features are calculated according to local derivative variations. Since LDP computes spatial relationship of neighbors in a local region, it extracts distinct information from the depth map. Multiscale depth-LDP is presented as a novel descriptor for 3D face recognition. The descriptor is employed along with the SRC to increase the range data distinctiveness. A histogram on the derivative pattern creates a spatial feature descriptor that represents the distinctive micro-patterns from 3D data. We evaluate the proposed algorithm on two famous 3D face databases, FRGC v2.0 and Bosphorus. The experimental results demonstrate that the proposed approach achieves acceptable performance under facial expression.