An Improved Contact-Based High-Resolution Palmprint Image Acquisition System

Abstract

Palmprints are a recognizable biometric feature that contains multimodal information. Contact-based high-resolution palmprints are the most commonly used image in automatic palmprint identification systems with high-level security requirements because of its high matching accuracy and robustness. Currently, the traditional contact-based high-resolution palmprint acquisition system only captures the total internal reflection (TIR) image, which is similar to fingerprint acquisition systems. However, it is worth noting that the palm is much larger than the finger, and different regions of the palm cannot be easily pressed to the same plane. We observed that the images obtained by these existing contact-based high-resolution palmprint image acquisition systems often have poor integrity and sharpness, especially in the center of the palm. Considering this problem, we proposed an improved contact-based high-resolution palmprint image acquisition system. In addition to the TIR image, it can also simultaneously acquire a diffuse reflection (DR) image. A high-resolution DR image has low contrast and is thus not suitable for direct identification. However, the high integrity and sharing of structural information with the TIR image make it possible to complement the TIR image. Our strategy is to transfer the DR image to the TIR domain through a convolution neural network (CNN) and then fuse it with the corresponding TIR image to obtain a higher quality TIR domain image. The partial-to-“full” identification experimental results show that the rank-1 accuracy of the fused TIR (FTIR) image is improved by approximately 23% compared with the traditional TIR image, which proves the effectiveness of our strategy.