Engin, M. Alptekin2024-10-042024-10-042021978-166543405-8https://doi.org/10.1109/ASYU52992.2021.9598960http://hdl.handle.net/20.500.12403/4036IEEE SMC Society; IEEE Turkey Section2021 Innovations in Intelligent Systems and Applications Conference, ASYU 2021 -- 6 October 2021 through 8 October 2021 -- Elazig -- 174400This paper presents extraction of rotation invariant features of handwritten signature images for classification. Initially, curvelet transform was applied to all signature image in the database. The mean and standard deviation values of each curvelet sub bands were used as features. Rotation invariance was obtained by applying cycle shift around the total spectral energy values of curvelet sub bands. Then, the classification process was carried out with the obtained features. The performance was compared with the method without cycle shift. As a result, it has been determined that the presented method gives the most successful accuracy using the support vector machine classifier. © 2021 IEEE.trinfo:eu-repo/semantics/closedAccesscurvelet transformimage classificationrotation invariant feature extractionConference Object10.1109/ASYU52992.2021.95989602-s2.0-85123215133N/A