Novel Segmentation of Iris Images for Biometric Authentication Using Multi Feature Volumetric Measure

Abstract

The aim of the research is to improve the efficiency of biometric authentication using different features of iris image. The biometric authentication and verification has become more popular where the authentication is more essential in many organizations. There are many approaches has been discussed to segment the iris image and to perform verification but suffers with the problem of accuracy in feature extraction and segmentation. To resolve such problems and to improve the efficiency of iris segmentation and recognition, we propose a novel segmentation algorithm which uses multi level filter which removes the eyelids and eyelash features and performs the edge detection to identify the inner and outer eye regions. Once the regions has been identified then, we compute various measures like the size of inner and outer eyes and extract the features of both and convert them in to feature vectors. The generated feature vectors are used to perform classification in biometric authentication approach. The multi feature volumetric measure is computed on the feature vector of each eye image where the feature vector has various features like the size of both inner and outer eyes, width and height, the original binary features, the number of binary ones and the number of pixels damaged by any form of disease and so on. Based on these features the MFVM is computed to classify the iris image towards a big data set of biometric features to perform authentication. The proposed method has improved the efficiency of iris segmentation and improved the efficiency of iris recognition based biometric authentication. Also the approach has reduced the time complexity and improved the efficiency also.