关键词: 人脸识别, 特征选择, 粗糙集理论, 属性全局重要度

Abstract: The feature selection in face recognition based on rough sets theory’s significance of attribute is proposed.At first，on the basis of PCA method，the feature vectors are extracted and the decision table of rough set is built.Then four definitions，which are classified significance and similar significance for single attribute and attribute subsets，are given respectively.At last，attributes reduction based on classified significance of attribute is proposed，and using similar significance of attribute，the final features for face image recognized classification are selected.The new feature selection method entirely relays on the minor knowledge of the data themselves.So the optimal feature set could be selected，and the face recognition precision could be improved.The experiment results show that the proposed method is superior to the traditional ones.

Key words: face recognition, feature selection, rough set theory, global significance of attribute