3D face recognition based on histograms of local descriptors

Face recognition in an uncontrolled condition such as illumination and expression variations is a challenging task. Local descriptor is one of the most efficient methods used to deal with these problems. In this paper, we present an automatic 3D face recognition approach based on three local descriptors, local phase quantization (LPQ), Three-Patch Local Binary Patterns (TPLBP) and Four-Patch Local Binary Patterns (TPLBP). Facial images are passing through one of the three descriptors and divided into sub-regions or rectangular blocks. The histogram of each sub-region is extracted and concatenated into a single feature vector. PCA (Principal Component Analysis) and EFM (Enhanced Fisher linear discriminant Model) are used to reduce the dimensionality of the resulting feature vectors. Finally, these vectors are sent to the classification step, when we use two methods; SVM (Support Victor Machine) and similarity measures. CASIA 3D face database is introduced to experimental evaluation. The experimental results illustrate a high recognition performance of the proposed approach.