Abstract: According to the demand of the high distinctive face features for face recognition, this paper proposes an improved algorithm based on Scale Invariant Feature Transform(SIFT) descriptor combined with a pattern matching strategy of K-means clustering for face recognition, it adopts the local and global similarity to calculate the matching for face images, and makes use of the square of similarity and the weight assignment solution based on probability statistics to improve accuracy in local feature matching. Experimental results show the robustness and effectiveness of the method.

Key words: face recognition, Scale Invariant Feature Transform(SIFT) feature, K-means clustering, similarity

摘要： 根据人脸识别中对高独特性的人脸特征的要求，提出一种改进的基于SIFT算子进行人脸识别的方法，结合K-means聚类的模式匹配策略，采用局部相似性和全局相似性的计算方法对人脸图像进行相似度匹配，并在匹配过程中使用基于概率统计的权值赋予方案和相似度的平方来提高识别的准确性。实验结果证明，该方法具备鲁棒性和有效性。

关键词: 人脸识别, SIFT特征, K-means聚类, 相似性

CLC Number: 

• TP18