SVM Based Gender Classification Using Iris Images
Atul Bansal, Ravinder Agarwal, Rajendra Kumar Sharma · 2012
These days biometric authentication systems based on human characteristics such as face, finger, voice and iris are becoming popular among researchers. These systems identify an individual as an authentic or an imposter using a database of enrolled individuals. These systems do not provide other information about imposter such as her gender or ethnicity. Various researchers have utilized facial images for gender classification. Iris images have also been used for identification but there exists a very few references reporting the identification of human attributes such as gender with the help of iris images. In this paper gender has been identified using iris images. Statistical features and texture features using wavelets have been extracted from iris images. A classification model based on Support Vector Machine (SVM) has been developed to classify gender and an accuracy of 83.06% has been achieved in this work.