On Generation and Analysis of Synthetic Finger-Vein Images for Biometrics Identification

The finger-vein biometric offers a higher degree of security, personal privacy and strong antispoofing capabilities than most other biometric modalities employed today. Emerging privacy concerns with the database acquisition and lack of availability of large scale finger-vein databases have posed challenges in exploring this technology for large scale applications. This technical report details the first such attempt to synthesize finger-vein images and presents analysis of synthesized images for the biometrics authentication. We generate a database of 50,000 finger-vein images, corresponding to 5000 different subjects, with 10 different synthesized finger-vein images from each of the subject. We use tractable probability models to compare synthesized finger-vein images with the real finger-vein images for their image variability. This report also presents matching accuracy of a biometric recognition algorithm, using the synthesized finger-vein database from 5000 different subjects, using 225,000 genuine and 1249,750,000 impostor matching scores, which suggests significant promises from this finger-vein biometric modality for large scale biometrics applications. This research also developed a user-friendly software to synthesize finger-vein image databases, which could help to advance further research in finger-vein biometrics.