Abstract:

In order to improve the accuracy rate of iris recognition,an improved curvelet transform algorithm for iris recognition was proposed.Firstly,the iris image was decomposed with fast discrete curvelet transform by wrapping algorithm.Mean variance and energy of curvelet sub-band coefficients in different scales and different orientations were extracted.The weights of sub-bands were estimated by generalized Gaussian distribution.The feature vectors with stronger classification ability had large weight,which were calculated to constitute feature vectors of iris image.Finally,feature vectors were matched and recognized by classifier combined with fuzzy support vector machine and binary decision tree.The algorithm performances were tested with UBIRIS and CASIA iris database.Simulation results show that the proposed algorithm has higher recognition accuracy rate and efficiency.It is feasibility.

Key words: generalized Gaussian distribution, iris recognition, curvelet transform, fuzzy support vector machine, binary decision tree