Extensive Analysis of Deep CNN-Based Multispectral Palmprint Verification System on All Fusion Levels

Palmprint is universal, unique, and acceptable biometric trait with higher reliability. Real world systems demand highly precision-wise identification systems; multispectral imaging provides dominant discriminative features and is robust against anti-spoofing. Each individual spectral band highlights different kinds of palm features. This paper proposes a CNN-based multispectral palmprint biometric system which is developed under a novel deep learning architecture that meets real-time application requirements. The adopted PolyU multispectral database consists of palmprint images collected under red, green, blue, and near-infrared (NIR) illuminations. We have investigated the performance of the system in individual spectral band and also analyzed the verification rate by combining images obtained from different spectral band. After analyzing the features from different spectral bands, we have developed biometric system under pre- and post-classification approaches to combine multispectral data.