Phase congruency features for palm-print verification

The existing palm-print verification schemes have demonstrated good verification performance when identity claims have to be verified based on palm-print images of adequate quality (e.g. acquired in controlled illumination conditions, free from distortions caused by the pressure applied to the surface of the scanner etc.). However, most of these schemes struggle with their verification performance when features have to be extracted from palm-print images of a poorer quality. In this study the authors present a novel palm-print feature extraction approach which deals with the presented problem by employing the two-dimensional phase congruency model for line-feature extraction. The proposed approach first computes a set of phase congruency features from a palm-print image and subsequently performs linear discriminant analysis on the computed features to represent them in a more compact manner. The approach was tested on two contrasting databases, namely, on the FE-LUKS and on the PolyU database. Encouraging results were achieved on both databases.