Iris recognition system for some clinical applications

Abstract

Today, with the increase in security threats all over the world authentication of an individual is becoming an important issue and area of interest for researchers. Over the traditional password or key based security systems biometric authentication systems are considered as very accurate and reliable. Iris Recognition System is one of them. It is very accurate system as iris images of twins or iris images of even left and right eye of same person are different. Numerous researchers have given iris recognition systems based on different feature extraction techniques. In this work, a comparative study of the existing techniques has been carried out. A simple, fast and effective statistical feature extraction based iris recognition system has been proposed and implemented. Features have been extracted in two different directions, namely, radial direction and angular direction. An attempt has been made to study the effect of number of features as well as the radial and angular resolution while normalization. Results obtained are effective, encouraging and comparable to existing techniques. In literature, little work has been reported on clinical applications of iris recognition systems. In this thesis, clinical applications of iris recognition system have also been investigated. Three different applications, i.e., to predict the gender of imposters, to predict diabetes and to predict obstructive lung disease have been considered. In security systems predicting gender of an imposter is equally important to determine the identity. Most of the work to predict the gender utilized facial images. A few studies have been reported using iris images. In the present research work, Support Vector Machine (SVM) based gender prediction model has been proposed and implemented. Results obtained show the effectiveness of system over the existing models. Further, a non-invasive and non-contact type model, i.e., a system as an aid to doctors is proposed to predict the disease from iris images.