Generating and analyzing synthetic finger vein images

The finger-vein biometric offers a higher degree of security, personal privacy and strong anti-spoofing 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 paper details the first 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 paper also presents matching accuracy 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 finger-vein biometric modality for the large scale biometrics applications.