Highly Accurate Multispectral Palmprint Recognition Using Statistical and Wavelet Features

Palmprint is one of the most useful physiological biometrics that can be used as a powerful means in personal recognition systems. The major features of the palmprints are palm lines, wrinkles and ridges, and many approaches use them in different ways towards solving the palmprint recognition problem. Here we proposed to use a set of statistical and wavelet-based features; statistical to capture the general characteristics of palmprints; and wavelet-based to capture those information which are not evident in the spatial domain. Subsequently we use two different classification approaches, minimum distance classifier (MDC) scheme and weighted majority voting algorithm (WMV), to perform palmprint recognition. The proposed method is tested on a well-known palmprint dataset of 6000 samples and shows an impressive accuracy rate of 99.65%-100% for most scenarios.