基于曲面等高线特征的不同姿态三维人脸深度图识别

3D Facial Depth Map Recognition in Different Poses with Surface Contour Feature

三维人脸识别因能克服二维人脸识别易受光照,姿态和表情等因素影响的缺点,从而日益受到关注和重视.文中针对三维人脸实时成像系统所获得的不同姿态下的三维人脸深度图,提出一种人脸识别方法(FDAC).首先利用微分几何相关理论来指导三维深度人脸深度图的校正,再根据曲面等高线来描述人脸的面部特征并使用傅里叶描绘子实现特征提取,最后利用提取的等高线特征进行人脸分类识别.实验结果表明,FDAC方法对于不同姿态下的三维人脸图像有较好的识别率,并且在时间开销方面优于常规的特征脸识别方法.

Three-dimensional face recognition has drawn more and more attention, for it overcomes the shortcomings of two-dimensional face recognition technology that two-dimensional face recognition is susceptible to the influence of light, expression changes and pose variations. A face recognition method, Fourier descriptor and contour （FDAC）, is proposed in this paper. It is based on the depth maps by the three-dimensional facial imaging system in different poses. Firstly, depth maps are corrected under the guidance of the differential geometry theory, and the human face features are described by the contours. Secondly, Fourier descriptor is employed to extract the facial features. Finally, these extracted features are used in the face recognition process. Experimental results show that FDAC has good recognition accuracy and it performs better in time cost compared with Eigenface method.