  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 have
proposed to use a set of statistical and wavelet-based features; statistical to
capture the general characteristics of palmprints; and wavelet-based to find
those information not evident in the spatial domain. Also we use two different
classification approaches, minimum distance classifier scheme and weighted
majority voting algorithm, to perform palmprint matching. The proposed method
is tested on a well-known palmprint dataset of 6000 samples and has shown an
impressive accuracy rate of 99.65\%-100\% for most scenarios.
