Finger-Knuckle Assisted Slap Fingerprint Identification System for Higher Security and Convenience

Every day, billions of fingerprint images are captured worldwide through the extensive deployment of slap-fingerprint acquisition devices, serving e-governance programs and bolstering national border security. Several studies from national ID programs, like UIDAI and NIST, have indicated that about 2% of the user population may lack usable fingerprints. Finger knuckle patterns are inherently presented during such slap-fingerprint acquisition and can be simultaneously acquired without imposing any additional inconvenience on the users. Leveraging these finger knuckle patterns can enable not only significant improvement in identification accuracy but also enhance overall protection and facilitates smoother traffic flow. This paper develops the first such finger-knuckle-assisted fingerprint identification system for real-world applications. We systematically develop automated finger knuckle detection and segmentation algorithms, for multiple knuckles and under complex illumination, for such contactless images from the deployed slap fingerprint devices. Currently, available algorithms offer limited performance for such images, and therefore this paper proposes a new approach to more accurately match such knuckle images. Our experimental results illustrate the significant performance improvement over existing knuckle matching algorithms, and further by incorporating dynamic fusion capabilities. This paper also introduces the first joint finger-knuckle and fingerprint database, from 120 different subjects, in the public domain to advance further research and development efforts needed in this area.