Explainable AI: A Multispectral Palm-Vein Identification System with New Augmentation Features

Recently, as one of the most promising biometric traits, the vein has attracted the attention of both academia and industry because of its living body identification and the convenience of the acquisition process. State-of-the-art techniques can provide relatively good performance, yet they are limited to specific light sources. Besides, it still has poor adaptability to multispectral images. Despite the great success achieved by convolutional neural networks (CNNs) in various image understanding tasks, they often require large training samples and high computation that are infeasible for palm-vein identification. To address this limitation, this work proposes a palm-vein identification system based on lightweight CNN and adaptive multi-spectral method with explainable AI. The principal component analysis on symmetric discrete wavelet transform (SMDWT-PCA) technique for vein images augmentation method is adopted to solve the problem of insufficient data and multispectral adaptability. The depth separable convolution (DSC) has been applied to reduce the number of model parameters in this work. To ensure that the experimental result demonstrates accurately and robustly, a multispectral palm image of the public dataset (CASIA) is also used to assess the performance of the proposed method. As result, the palm-vein identification system can provide superior performance to that of the former related approaches for different spectrums.