  Personal identification problem has been a major field of research in recent
years. Biometrics-based technologies that exploit fingerprints, iris, face,
voice and palmprints, have been in the center of attention to solve this
problem. Palmprints can be used instead of fingerprints that have been of the
earliest of these biometrics technologies. A palm is covered with the same skin
as the fingertips but has a larger surface, giving us more information than the
fingertips. The major features of the palm are palm-lines, including principal
lines, wrinkles and ridges. Using these lines is one of the most popular
approaches towards solving the palmprint recognition problem. Another robust
feature is the wavelet energy of palms. In this paper we used a hybrid feature
which combines both of these features. %Moreover, multispectral analysis is
applied to improve the performance of the system. At the end, minimum distance
classifier is used to match test images with one of the training samples. The
proposed algorithm has been tested on a well-known multispectral palmprint
dataset and achieved an average accuracy of 98.8\%.
