سیستم جدید احراز هویت مبتنی بر تبدیل های کانتورلت و موجک برای تصاویر با کیفیت پائین کف دست

This paper proposed an authentication system based on low quality palmprint. For implementation of this system, first the features is extracted by using Contourlet and wavelet transforms. In the second phase, some of the features are selected by using Across Group Variance (AGV) filter. In the last phase by using a classification method, the authentication is completed. For classification we evaluated three different methods, Support Vector Machine (SVM), Revised Nearest Neighbor (RNN), and Boosted Direct Linear Discriminant Analysis (BDLD). The experiment is performed on the famous PolyU Palmprint database. The results shows that by combination of the proposed system and BDLD classifier has better performance in comparison to other methods and the same database.