A novel approach based on high order tensor and multi-scale locals features for 3D face recognition

This paper presents an efficient framework for verification using 3D information based on high order tensor representation in uncontrolled conditions. The 3D depth images are subdivided into sub-blocks and the Multi-Scale Local Binarised Statistical Image Features (MSBSIF) + Multi-Scale local phase quantization (MSLPQ) histograms are extracted and concatenated from each block and organized as a 3rd order tensor. Moreover, two steps of dimensionally reduction to the face tensor are used. Firstly, Multilinear Principal Component Analysis (MPCA) is used to project the face tensor in a new subspace features in which the dimension of each mode tensor is reduced. After that, Enhanced Fisher Model (EFM) is applied to discriminate the faces of diverse persons in the database. Finally, the corresponding is achieved based distance measurement. The proposed approach (MPCA+EFM) has been evaluated on the challenging face database Bosporus 3D. The experimental results demonstrate that our method attains a high authentication performance.