3D Pose Of Face Tracking Based On Feature Matching

Human face is our primary focus of attention in social intercourse,playing a major role in conveying identity and emotion. Facial features having high precision^ easy collection rich in content and wide applicability, etc. Making a face has become the most popular biometric when people confirm a person’s identity. Image sequence of face detection, tracking and recognition is accompanied by increased demand for rapid development of computer hardware and visual technology applications. These three technologies complement each other,face recognition and verification has become daily life skills in modern society,such as security video surveillance, access control systems, video conferencing, identification and other key technologies. Research on the face tracking,recongnition technology has great theoretical and practical value.In this paper,aiming at the problem of object face tracking in the video sequence, an approach was proposed for 3D pose tracking with a single camera, and the paper introduces the general 3D face mesh model. The tracking works totally involves the following aspects:1.Introducing several widely used face tracking algorithm, such as optical flow, Camshift and particle filter,and analyze their advantages and limitations.By analysing the limitations of the above algorithm,so we come up with the 3D face pose tracking.2.The second part is the human face feature extraction and location,we select the ASM algorithm to form the shape of the object model for initializing face tracking.3.The third part is the inter-frame motion matching algorithm.PC-SIFT(Principal Component-Scale-invariant feature transform)algorithm is also used which can get reliable inter-frame feature match.And fusing the feature correspondence information from either previous frame or some selected key-frame into the currunt frame pose estimation. Pose estimation is a classical problem which can be formulated as that of minimizing an error metric based on collinearity in object.4.The fourth part is dealing with the pose estimation,current face pose estimation is obtained via 3D project from combination RANSAC and POSIT algorithm.Various sets of experimental data shows improvement of our algorithm over existing 2D matching algorithms especially in solving the pose drifting question when tracking agile motion,severe occlusion,drastic illumination change.By comparing multiple sets of experimental data,we show that the algorithm in severe occlusion, head swing severely,the match point with less complex cases interference is still robust,and compared to conventional 2D tracking algorithm the 3D face tracking algorithm solves the problem of drift,achieve the goal of face tracking stability, has significantly improved in a complex environment.