Toward Pose Invariant and Completely Contactless Finger Knuckle Recognition

Abstract

In order to advance application of finger knuckle modality in new domains, especially using widely popular smartphones, completely contactless and pose invariant knuckle identification are highly desirable. Earlier work in the literature incorporates finger knuckle images under fixed poses, which is not realistic for completely contactless biometrics applications. This paper makes a first such attempt to investigate the possibility of recognizing completely contactless finger knuckle images acquired under varying poses. New approach to automatically normalize and align contactless finger knuckle images is introduced, and the performance of a specialized matcher is investigated to achieve superior performance. A new database from 221 different subjects’ contactless finger knuckle images is also developed and made available in public domain to advance much needed research in this area. Experimental results illustrated in this paper are promising and validate the usefulness of normalization and matching algorithms to recognize finger knuckle with different poses. Spatial and spectral domain features can provide complimentary cues and are simultaneously employed for the performance improvement. Such performance improvement only serves as the baseline for much needed further improvement in recognition accuracy for database presented in this paper.