Biometric recognition using digital curvelet transform and BP neural network

The theoretical studies indicate digital curvelet transform to be an even better method than wavelets for optical application. In this paper, a multiscale biometric recognition method based on digital curvelet transform via wrapping is surveyed and studied. First, all images are decomposed by using curvelet transform. As a result of performing curvelet transform, curvelet coefficients of low frequency and high frequency in different scales and various angels will be obtained. Then, low frequency coefficients as study samples to the BP neural network are applied. Finally, low frequency coefficients of testing image are used to simulate neural network, then recognition results will be obtained. The experiments are performed on the Cambridge University ORL database, and the results show that the recognition rate of the curvelet-based method is obviously improved. © 2009 SPIE.