Abstract: In the global feature extraction and recognition of finger knuckle print based on PCA ( Principal Component Analysis) ，in order to improve the accuracy，we prove that the knuckle patterns of four fingers have different classification weights，i． e． contribution rate to identification results according to researches on traditional methods． This way improved the equal weight distribution method shown in existing research consequences． The experiment results show that when the four-finger weight ratio is 2 ∶ 2 ∶ 3 ∶ 2，the maximum recognition rate，which is 94． 4%，can be achieved when 21 dimensional characteristics are used． In contrast，94． 4% can be achieved when 32 dimensional characteristics are used in the equal weight distribution．Therefore，this method can reduce feature dimensions and improve the recognition speed．

Key words: finger knuckle print, global feature extraction, feature recognition, principal component analysis( PCA)