基于表情变化的三维人脸识别  

3D Face Recognition Based on Expression Change

作　　者：黄敏[1] 宫秋萍 曾莎[1] 

机构地区：[1]郑州轻工业学院计算机与通信工程学院,郑州450002

出　　处：《现代计算机（中旬刊）》2015年第2期53-57,共5页Modern Computer

基　　金：国家自然科学基金青年科学基金项目(No.61201447);河南省基础与前沿技术研究计划项目(No.102300410266;No.122300410287)

摘　　要：为了减小表情变化对三维人脸识别带来的影响,提出一种由粗到细的识别方法。以人脸的深度数据为整体特征,采用Fisherface(PCA+LDA)方法进行匹配,以面部刚性区域作为局部特征采用改进的迭代最近点(ICP)算法进行比配,将得到的整体特征和局部特征进行融合。实验结果表明,该方法能有效提高人脸识别系统针对表情变化的鲁棒性。In order to reduce the impact of changes in the expression of 3D face recognition, presents a coarse to fine identification methods. Takes depth data as the overall features of a human face, uses Fisherface to match these features. The facial rigid region as local feature is matched using the modified iterative closest point algorithm. The extracted matching results of the global and local features are fused. The experimental result shows that the method has better robustness to facial expression change.

关 键 词：三维人脸识别 Fisherface(PCA+LDA) 深度数据 刚性区域 ICP 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]