Automatic Feature Reduction Framework For Identification Process In Palm Vein Recognition

Prasti Eko Yunanto, Hertog Nugroho, Tjokorda Agung Budi Wirayuda



Feature or dimensionality reduction has become one of fundamental problem in the field of pattern recogni- tion such as biometrics. The choosing number of fea- ture or dimension has become one challenge. Instead choosing number of feature manually, in this paper, we proposed an automatic feature reduction by using a cas- caded feature reduction schemes based on variance or- der of the DCT feature space and eigenvalue of k-PCA in the palm vein recognition. Based on experiment re- sults, our proposed scheme can achieve recognition rate above 0.92 accuracy which uses fewer features and can reduce time process significantly until 99.5% comparing with traditional manual feature reduction method.

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