Toward More Accurate Matching of Contactless Palmprint Images Under Less Constrained Environments

Abstract

Contactless personal identification using biometrics characteristics brings multifaceted advantages with improved hygiene, user security, and the convenience. Such imaging also generates deformation-free palmprint images which can lead to higher matching accuracy as the ground truth information is better preserved as compared with those from contact-based imaging. Advancement of palmprint identification technologies for new domains requires research using larger palmprint databases that are acquired from more realistic populations, under contactless, ambient, and indoor and outdoor environments. This paper presents such a new contactless palmprint database acquired from 600 different subjects, which is the largest to date and is also made available in the public domain. Unlike contactless fingerprints, contactless palmprint images often illustrate pose deformations along the optical axis of the camera, which also degrades the matching accuracy. This paper also introduces a new approach for matching contactless palmprint images using more accurate deformation alignment and matching. The experimental results are validated on three publicly available contactless palmprint databases. Comparative experimental results presented in this paper indicate consistently outperforming results over competing methods in the literature and validate the effectiveness of the investigated approach. These results also serve as baseline performance to advance much needed further research using the most challenging and largest database introduced from this paper.