Biometric identification from three-dimensional (3-D) face surface characteristics has become popular. Traditional face recognition methods have achieved very high recognition accuracy under controlled environments. However, 3-D face recognition technology still faces a great challenge for facial expressions and missing data caused by pose variations or occlusions. The mesh-based scale-invariant feature transform (SIFT)-like method is one of the most effective methods to deal with these issues. However, eliminating edge response has not been considered in this kind of method, which affects recognition performance and computation cost. To address this problem, a mesh edge point filter is proposed to remove edge keypoints. For feature description, a local descriptor that is composed of the histogram of geometric shapes and the histogram of shape index is designed to describe local shapes of keypoints. A complete mesh-based SIFT-like framework for 3-D face recognition is also presented. Results from experiments based on the Bosphorus and face recognition grand challenge v2.0 databases show that the proposed method is effective for expression variations, occlusions, and pose variations. Compared with several state-of-the-art methods, the proposed method has obtained a great compromise between computational complexity and recognition performance.

中文翻译：

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表达式和缺失数据下基于网格的尺度不变特征变换类三维人脸识别方法

从三维（3-D）面部表面特征进行生物特征识别已变得很流行。传统的人脸识别方法在受控环境下已经实现了很高的识别精度。然而，由于姿势变化或遮挡而导致的面部表情和数据丢失，3-D人脸识别技术仍然面临巨大挑战。基于网格的尺度不变特征变换（SIFT）类方法是解决这些问题的最有效方法之一。但是，在这种方法中没有考虑消除边缘响应，这会影响识别性能和计算成本。为了解决这个问题，提出了网格边缘点过滤器以去除边缘关键点。有关功能说明，设计由几何形状的直方图和形状索引的直方图组成的局部描述符，以描述关键点的局部形状。还介绍了用于3D人脸识别的基于网格的完整SIFT类框架。基于Bosphorus和人脸识别Grand Challenge v2.0数据库的实验结果表明，该方法可有效处理表情变化，遮挡和姿势变化。与几种最先进的方法相比，该方法在计算复杂度和识别性能之间取得了很大的折衷。0数据库表明，所提出的方法对于表情变化，遮挡和姿势变化有效。与几种最先进的方法相比，该方法在计算复杂度和识别性能之间取得了很大的折衷。0数据库表明，所提出的方法对于表情变化，遮挡和姿势变化有效。与几种最先进的方法相比，该方法在计算复杂度和识别性能之间取得了很大的折衷。