On the Use of Convolutional Neural Networks for Palm Vein Recognition

Palm vein recognition is a biometric method for individuals’ authentication and/or identification based on the unique patterns of veins in the palms of their hands. This paper presents a comprehensive study of the Convolutional Neural Network (CNN) for palm vein recognition. Several CNN architectures, such as VGGNet, AlexNet, and ZFNet, have been studied and adapted by proposing an improved version based on the optimization of their parameters. Two hybrid approaches based on the fusion of the improved versions of these CNN models are proposed. The proposed method was evaluated on near-infrared palm vein images from MS-PolyU database using data augmentation. The experimental results carried out have shown the high accuracy of the proposed method, allowing achieving an accuracy rate of up to 99.72%.