PALMPRINT RECOGNITION BASED ON DISCRETE CURVELET TRANSFORM AND SUPPORT VECTOR MACHINE

A multiscale palmprint recognition method based on discrete curvelet transform and support vector machine was proposed. First, all palmprint images were decomposed by using discrete curvelet transform via wrapping. As a result, curvelet coefficients in different scales and various angels were obtained. The important feature information of palmprint was included in the low frequency coefficients of curvelet transform decomposition coefficients. After the decomposition coefficients were transformed into feature vectors, they were regarded as feature parameters and sent into SVM for training. Finally, SVM was used for classification of palmprint. The experiments were performed in the PolyU Palmprint database. The results indicate that the proposed method has better performance than wavelet-based method and other classical methods.