Finger-vein recognition based on dual-sliding window localization and pseudo-elliptical transformer

Abstract

Uneven illumination occurs during finger imaging because of the influence of several factors, including the position and posture of the finger, the uniformity of near-infrared light, and the influence of ambient light. Existing phalangeal joint locating methods are sensitive to light illumination and cannot locate phalangeal joint stably. In this study, we propose a dual-sliding window model to accurately detect the position of the phalangeal joint of the finger-vein image, which is robust to light illumination, and to extract a more stable region of interest. Planar imaging generates different finger-vein images of the same finger at different acquisitions by space rotation of the finger. Thus, a pseudo-elliptical sampling model is proposed to retain the spatial distribution of vein patterns, to reduce the redundant information in finger images, and to reduce differences. Finally, a two-dimensional principal component analysis is used to project the transformed image for feature extraction. We calculated the Euclidean distance to measure the similarity between the test and training samples. Experiments in the three different databases show that the proposed method is effective and reliable and improves the performance of a finger-vein identification system.