  Biometric-based identification has drawn a lot of attention in the recent
years. Among all biometrics, palmprint is known to possess a rich set of
features. In this paper we have proposed to use DCT-based features in parallel
with wavelet-based ones for palmprint identification. PCA is applied to the
features to reduce their dimensionality and the majority voting algorithm is
used to perform classification. The features introduced here result in a
near-perfectly accurate identification. This method is tested on a well-known
multispectral palmprint database and an accuracy rate of 99.97-100\% is
achieved, outperforming all previous methods in similar conditions.
