Expression-robust 3D face recognition based on feature-level fusion and feature-region fusion

3D face shape is essentially a non-rigid free-form surface, which will produce non-rigid deformation under expression variations. In terms of that problem, a promising solution named Coherent Point Drift (CPD) non-rigid registration for the non-rigid region is applied to eliminate the influence from the facial expression while guarantees 3D surface topology. In order to take full advantage of the extracted discriminative feature of the whole face under facial expression variations, the novel expression-robust 3D face recognition method using feature-level fusion and feature-region fusion is proposed. Furthermore, the Principal Component Analysis and Linear Discriminant Analysis in combination with Rotated Sparse Regression (PL-RSR) dimensionality reduction method is presented to promote the computational efficiency and provide a solution to the curse of dimensionality problem, which benefit the performance optimization. The experimental evaluation indicates that the proposed strategy has achieved the rank-1 recognition rate of 97.91 % and 96.71 % based on Face Recognition Grand Challenge (FRGC) v2.0 and Bosphorus respectively, which means the proposed approach outperforms state-of-the-art approach.