2D and 3D face recognition based on IPC detection and patch of interest regions

In this paper, we propose a framework of Face Recognition System (FRS). Essentially, we are focused on the face detection process and the role of interest regions of the human face. In order to locate exactly the facial area, we propose the use of horizontal and vertical IPC (Integral Projection Curves). The role of important patches of face: nose and eyes is investigated in this work. An efficient method based on PCA (Principal component analysis) followed by EFM (Enhanced Fisher Model) is used to build the characteristic features, these latter are sent to the classification step using two methods, Distance Measurements and SVM (Support Vector Machine). Finally, the effect of fusion of two modalities (2D and 3D) is studied and examined. Experiments are performed on the CASIA3D face database which contains 123 persons under varying of illumination, expression variation.