Face recognition using a new feature selection method
Di Xiao, Lin Jin-guo · 2008
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 for significance of attributes, which are classifiable significance and similar significance for single attribute and attribute subsets, are given respectively. At last, attributes reduction based on classifiable 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 apriority knowledge of the data themselves. So the optimal feature subset 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.