A Research On 3D Face Recognition

Face recognition is one of the most hot and challengeable technologies, which based on biometrics, and also one of the most potential technologies. As the most natural and friendly way, automatic face recognition has become the important part of the Next Generation Computing. 3D face recognition methods are in general able to overcome the problems resulting from illumination, expression or pose variations in 2D face recognition. This thesis address itself to the task of 3D face recognition. The main contributions of the work are as follows:(1) Gridding resample based on separate block is proposed, which is used to standardize the data generated from diversified methods. And a data struct of triangle index is designed. The storage quantity is reduced much, and the convenience of the manipulation is improved.(2) Radial difference image and improved Gaussian image are used to detect the edge of face features. Region growth is proposed to construct the facial feature. The algorithms of fitting region edge and optimizing energy function are presented to extract the accurate feature points. And providing a manual way to adjustment the feature points.(3) An technology on 3D face recognition research. Use ICP algorithms matching face features models, Hausdorff distance as the measurement means to achieve face recognition.(4) Besides theory research, we also built a 3D face recognition system, which named FaceRecognition, using Visual C++ 6.0 and OpenGL on the PC platform. System use the method mention in paper, the result of the test has verified its feasibility.