3D and 2D face recognition using integral projection curves based depth and intensity images

Abstract

This paper presents an automatic face recognition system in the presence of illumination, expressions and pose variations based on depth and intensity information. At first, the registration of 3D faces is achieved using iterative closest point ICP. Nose tip point must be located using Maximum Intensity Method. This point usually has the largest depth value; however there is a problem with some unnecessary data such as: shoulders, hair, neck and parts of clothes; to cope with this issue, we propose the integral projection curves IPC-based facial area segmentation to extract the facial area. After that, the combined method principal component analysis PCA with enhanced Fisher model EFM is used to obtain the feature matrix vectors. Finally, the classification is performed using distance measurement and support vector machine SVM. The experiments are implemented on two face databases CASIA3D and GavabDB; our results show that the proposed method achieves a high recognition performance.