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Eigenbased Multispectral Palmprint Recognition

Volume 2 - Issue 2, February 2018 Edition
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Author(s)
Abubakar Sadiq Muhammad, Fahad Abdu Jibrin, Abubakar Sani Muhammad
Keywords
Palmprint Verification, Multispectral Image Fusion, Eigenpalm, Distance metric Classifiers.
Abstract
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.
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