关键词: 特征点标定, 统计可变形模型, 三维人脸, 遗传算法

Abstract: Automatic 3D facial feature point extraction is a hot field of computer vision, which is widely used in face recognition, face model alignment, facial expression recognition, facial animation and other areas. By means of the statistical modeling of 3D face samples and the parameter optimization challenge in the number of for-matched models is solved by Genetic Algorithm（GA）. The 3D facial feature points can be calibrated automatically by using the model similarity matching method and its mapping relation. Firstly, the statistic models of 3D face model are constructed. Then get the mapping relation of the reference model and the matching model by the model deformation and then using the parameter about the number of for-matched model by Genetic Algorithm（GA） to generate the corresponding matching model. Secondly, calculate the similarity between the test model and matched model. Finally, the feature points of the model to be measured are indirectly obtained with the model similarity and the projection relationship. The experimental results show that the proposed algorithm is feasible and very effective. The method can automatically extract 3D face feature points. When the threshold value of the distance is 10 pixels, 39 of the 3D face feature points localization accuracy in this article can reach 100%. At the same time it can also solve the problem of traditional method of 3D face model smooth area feature point with low precision effectively.

Key words: feature point extraction, statistical deformable model, 3D face, Genetic Algorithm（GA）