英文摘要 | Both face 3D reconstruction and recognition based on images are the significant researchs in the fileds of Computer Vision, Computer Graphics, and Pattern Recognition. As the primary components of Face Perception, these two technologies have broad application prospects in identity authentication, video surveillance, face animation, human computer interaction, and so on. Currently, the start-of-art face reconstruction and recognition system achieves satisfied results under the well-controlled environment. However, they are still challenged by the facial smooth texture and various image environments, such as occluded, posture, illumination or heterogeneous capturing conditions. With a thorough review of previous works, this paper researchs the issues of face 3D reconstruction and its supplements on face image recognition, which is based on the following starting points, that the accuracy and efficiency of facial shape reconstruction is different under the various applications, which leads to the employment of kinds of face 3D reconstruction methods, also facial shape can effectively compensate the texture loss in face imaging, while face is known as 3D nonrigid object. In particular, the main contributions of this thesis include following parts: 1) Face 3D reconstruction based on deformable model. Firstly, to avoid the drawbacks of initialization sensitivity and low location precision of profile face contour, a multi-layer ASM based on keypoints location and 3D shape adjustment is proposed as the basis of face 3D reconstruction, while the location precision of keypoints is better than others. In shape initialization, keypoints are located firstly to fit a RBF model for the transformation of other points. Then the 3D facial posture is estimated to take the place of the original 2D shape adjustment and compensate the misregistration of contour points. Both of them improve the traditional ASM location precision and stability. Secondly, to handle with the model distortion in the directly transformation based on 2D shape points, the original image is aligned based on the 3D estimation posture, which mproves the flatness of the releastic model. 2) Face 3D reconstruction based on binocular stereo vision. Firstly, a keypoints matching approach based on LOG detector and RBF extension are carried out to generate denser and acattered matching pairs on the sparse facial texture, which enhance the points density in the regular SIFT detector. Also the RBF mapping bet... |
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