Palm vein recognition technology is a one of the most effective biometric technologies for personal identification. Palm acquisition techniques are either contact-based or contactless-based. The contactless-based palm vein system is considered more accurate and efficient when used in modern applications, but it may suffer from problems like pose variations and the delay in the matching process. This paper proposes a contactless-based identification system for palm vein that involves two main steps; First, the central region of the palm is cropped using fast extract region of interest algorithm, then the features are extracted and classified using altered structure of Residual Attention Network, which is a developed version of convolutional neural network that uses an attention mechanism. The altered structure is constructed by stacking multiple attention modules and pre-activation residual unit with additional down sampling layers in between. The proposed system was tested on contactless CASIA multispectral palm vein databases that contains palm images with obvious pose variations taken from 100 persons. The results show that our system has outperformed other state-of-the-art systems with 95.55% accuracy and fast identification process of 0.06 second per person.
