Palmprint enhancement network (PEN) for robust identification

Abstract

AbstractDespite being reliable, palmprints have not received as much attention as other biometrics such as fingerprints, face or iris. Amount of information provided by high resolution palmprints and the fact that they have huge forensic value makes them a preferred biometric choice for large scale identification systems. In palmprints, extraction of reliable features for identification is still a challenging task especially because most of palmprints found in the real world, e.g., in crime scenes, are of poor quality. This makes palmprint enhancement a crucial pre cursor to identification. Errors during enhancement result in extraction of un-reliable features which deteriorate identification accuracy. Recent works in palmprints have focused more on matching algorithms and limited novelty has been introduced in enhancement. Enhancement techniques used on high resolution palmprints recently are either borrowed from fingerprint techniques or are built on the high-risk assumption that palm ridge pattern is stationary or smooth in a local area. Large size and abruptly changing ridge pattern of palmprints dictates the need for a more robust enhancement scheme. This paper proposes a novel deep learning based high resolution palmprint enhancement approach that is able to process large areas of palmprint without making the assumption that underlying ridge pattern is stationary. We have tested proposed enhancement approach on a renowned high resolution palmprint dataset which shows that proposed technique performs favourably in comparison to state of the art.