Using SIFT features in palmprint authentication

Abstract

As a new branch of biometrics, palmprint authentication has attracted increasing amount of attention because palmprints are abundant of line features so that low resolution images can be used. In this paper, we present two novel approaches for palmprints authentication. Firstly, we employ the SIFT (Scale Invariant Feature Transformation) for palmprint authentication. Point-wise matching is used to match SIFT key points extracted form palmprint images. Secondly, we extend a time series technology, SAX (Symbolic Aggregate approximation), to 2D data for the palmprint representation and matching. Using a public palmprint database, we demonstrate that the two proposed approaches, when combined together, can achieve the palmprint authentication accuracy comparable to that of the state of the art algorithms.