高独特性特征的选择以及合适匹配策略的选用是人脸识别技术的关键。讨论了基于仿射不变的几何特征SIFT算子进行人脸识别的方法。SIFT算子的计算复杂度较高，并且不同的人脸表情和图像模糊会加大特征匹配的难度。为克服上述缺点，提出了一种新的算法，将选择6个人脸上感兴趣子区域进行描述，并根据各自的独特性赋予不同的权值，最后在匹配过程中使用相似度的平方来减小偏差数据造成的影响。实验结果表明，该方法能有效减轻表情变化对于身份识别率急剧下降的影响，并可显著减少计算复杂度和特征匹配时间。

Choosing a distinctive feature and matching criterion is key to developing a reliable face recognition system. This paper discusses the availability of one of geometric feature invariants, scale invariant feature transform (SIFT) descriptor based face recognition. The SIFT feature description of an image is typically complex. In most cases, the difficulty of feature matching problem is aggravated when the different face expressions and image blur exist. For abovementioned issues, in this paper we proposes a new method that six interest sub regions from the face are selected to be described and later be calculated through different weights according to their distinctiveness. The square of the similarity is used to solve the problem of data deviation. The experimental results demonstrate that our method does effectively moderate the face expression effect. It also successfully reduces the complexity and matching time of SIFT feature sets.