Multimodal face recognition based on histograms of three local descriptors using score level fusion

In this paper, we propose an efficient framework of multimodal face recognition that explores 2D and 3D information based on the score level fusion. To solve the problems of illumination and expression variations, three local methods are introduced, Local Phase Quantization (LPQ), Three-Patch Local Binary Patterns (TPLBP) and Four-Patch Local Binary Patterns (TPLBP). After applying local descriptors to the input image (2D and 3D), this latter is divided into sub-regions or rectangular blocks. Then, the histogram of each sub-region is extracted and concatenated into a single features vector. Principal Component Analysis (PCA) and Enhanced Fisher linear discriminate Model (EFM) are used to reduce the dimensionality. Classification is then performed using the robust Support Vector Machine (SVM) classifier. Finally, score level fusion is used to improve the recognition performance. Experiments are implemented on CASIA3D face database. Our results show that the proposed approach achieves very high performance with RR=98.65% and EER=0.67%.