Accurate And Real-Time Face Recognition Based On Near-Infrared Image

In recent years, biometric identification technology is getting increasingly attention in public security systems. Comparing with other biometric technology, face recognition has some advantages, such as easy to use, harder to circumvent etc. Now, it is widely used in society.The variations of illumination and pose will affect the performance of the face recognition system seriously. Using active Near-Infrared Imaging system can solve the problem caused by variations of illumination effectively. However, the variation of pose cannot be controlled directly; we can estimate the face pose to solve the problem. The task of face pose estimation is to determine the pose of the input face image in 3-D space.This thesis proposes a novel face pose estimation method, which is used for determinate rotation angle of a given face. The novel method consists of edge statistical-based face pose estimation process and feature location-based face pose estimation process. The edge statistical-based face pose estimation process uses the overall information to extract the features firstly, and then the linear regression algorithm is applied to build the corresponding relationship between features and poses. Feature location-based face pose estimation process is another process proposed in this thesis, which is a local information-based method utilizing mathematical morphology operator to pinpoint the nostril. Then according to the location of the nose and eyes, face poses are estimated.In this thesis, LBP feature and AdaBoost algorithm is used to train a classifier, which can discriminate the label of the input pattern. In the process of face recognition, the input face image was estimated by the method mentioned above. If the rotation angle of the face exceeds the threshold, the image is discarded. The pre-judgment process improves performance of the face recognition system.The trained classifier was tested in a large scale database, experimental results shows that the face recognition method with pre-judgment of pose estimation outperforms traditional method.