Science and Education Publishing Journal of Computer Sciences and Applications 2328-725X 3 2 2015 03 28 Palmprint Recognitionvia Bandlet, Ridgelet, Wavelet and Neural Network 23 28 EN Mohanad A. M. Abukmeil Electrical Engineering, Islamic University of Gaza, Gaza, Palestine Hatem Elaydi Electrical Engineering, Islamic University of Gaza, Gaza, Palestine Mohammed Alhanjouri JCSA2015321 10.12691/jcsa-3-2-1 2015 02 22 2015 03 09 2015 03 28 Palmprint recognition has emerged as a valid biometric based personal identification tool. Palmprints with high resolution features such minutia points, ridges and singular points or low resolution features such as wrinkles and principals determine their applications. In this paper a 700nm spectral band PolyU hyperspectral palmprint database is utilized and the multiscale band let image transform is utilized in features extraction; moreover, its results are compared with the ridgelet and 2D discrete wavelet results. The size of features is reduced using principle component analysis and linear discriminate analysis; in addition, a feed forward back-propagation neural network is used as a classifier. The results show that the recognition rate accuracy of the band let transform outperforms others.