International Science Community Association Research Journal of Recent Sciences 2277 - 2502 3 4 2014 April 2 Predicting Gender Using Iris Images 20 26 EN ZolfaghariZaferani Rashid 1 Faculty of Education And Consulting, Roudehen Branch, Islamic Azad University, Roudehen, IRAN Haji AliakbariNeda 2 HosseinRezaeiDolat Abadi 1 Islamic Azad University, Persian GULf International Educational Branch, Khuzestan, IRAN AliReza Eghbali 2 HassanHeidarySoltan Abadi 3 EspananiHamid Reza 1 Department of Biology, Faculty of Sciences, Payam Noor University of Iran, Employee social security, Isfahan, IRAN Shirani Kobra 2 Department of Pharmacodynamy and Toxicology, School of Pharmacy, Mashhad University of Medical Sciences, IRAN Khalilian Saadat 3 Department of Biochemistry, Faculty of Biological Science, Tarbiat Modares University, Tehran, IRAN Sadeghi Leila 4 Department of Biology, Faculty of Sciences, Payam Noor University of Iran, Isfahan, IRAN YousefiBabadiVahid 5 Physiology Research Center, Isfahan Cardiovascular Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical AmraeaiEsmaiel 6 Bansal A. 1 Department of ECE, G.L.A. University, Mathura, UP, INDIA Agarwal R. 2 Department of EIED, Thapar University, Patiala, Punjab, INDIA Sharma R.K. 3 School of Mathematics and Computer Applications, Thapar University, Patiala, Punjab, INDIA > Research Paper 2013 8 9 2014 April 2 Among various biometric authentication systems iris recognition system is considered to be more accurate and reliable. The main objective of these systems is to identify the user as an authentic or an imposter. These systems does not reveal about imposter’s gender or ethnicity. Majority of practices for gender classification utilize facial information. Very few references in the literature reported the identification of human attributes such as gender with the help of iris images. In this paper gender has been identified using iris images. Feature vector from an iris image is created by combining statistical features and texture features using wavelets. A gender prediction model using Support Vector Machine (SVM) has been developed and an accuracy of 85.68% has been achieved. Copyright@ International Science Community Association