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中文摘要：
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。
英文摘要：
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.
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