Inner-knuckle-print verification based on guided image filtering

Abstract

This paper presents a new approach for inner-knuckle-print verification. Firstly, guided image filtering is implemented to remove noise and the minute lines. Then robust line features are extracted from the image based on a derivative edge detector. Finally the binary line images are matched by using a cross-correlation-based method. The experiments on a finger image database which includes 2000 images from 100 different individuals show good performance of the proposed approach. © 2013 Springer-Verlag.