Adaptive feature selection based on reconstruction residual and accurately located landmarks for expression-robust 3D face recognition

A novel adaptive feature selection based on reconstruction residual and accurately located landmarks for expression-robust 3D face recognition is proposed in this paper. Firstly, the novel facial coarse-to-fine landmarks localization method based on Active Shape Model and Gabor wavelets transformation is proposed to exactly and automatically locate facial landmarks in range image. Secondly, the multi-scale fusion of the pyramid local binary patterns (F-PLBP) based on the irregular segmentation associated with the located landmarks is proposed to extract the discriminative feature. Thirdly, a sparse representation-based classifier based on the adaptive feature selection (A-SRC) using the distribution of the reconstruction residual is presented to select the expression-robust feature and identify the faces. Finally, the experimental evaluation based on FRGC v2.0 indicates that the adaptive feature selection method using F-PLBP combined with the A-SRC can obtain the high recognition accuracy by performing the higher discriminative power to overcome the influence from the facial expression variations.