Eigenbased Multispectral Palmprint Recognition

This paper presents a simple method for verification of palm images. Fusion of palmprint instances is performed image level. To capture the palm characteristics the fused image were concatenated to form longer feature vector whose dimension is reduced by Principal Component Analysis PCA. Finally the reduced set of features is trained with distance metric classifiers Manhattan Euclidean and Cosine Distance to accomplish recognition task. For evaluation PolyU Multispectral Palmprint database is used. The experimental results reveal that three bands R B NIR contain most of the salient and discriminative features for building an accurate biometric system and in which a recognition rate of 99.99 can be achieved.