Certain Investigations on the Hybrid Methodologies for IRIS Recognition

Abstract

Biometric refers to the process of identifying everyindividuals based on their unique features in terms of both behavioural and physiological characteristics. Behavioural characteristics mainly includesignature, gait and voice recognition whereas physiological characteristics includeface, iris, ear, retina, hand and fingerprint recognition respectively. Out of these various biometric traits, iris recognition is considered to be the most reliable and robust recognition methods for verifying and identifying the exact individuals. Various process in biometric iris recognition includes, eye image acquisition, image pre-processing, image segmentation, normalization, feature extraction, classification and matching. Many state-of-the-art algorithms were developed for iris recognition in past two decades. On the other hand, from the literature it is evident that, only few hybrid algorithms were focused for recognizing the exact individuals.Hence, this thesis proposesnew hybrid iris recognition methods for recognizing the individual more accuratelyunder unconstrained environments that refers toocclusiondue to upper and lower eyelid, specular reflections, eyelashes etc., The goal of this research work is to propose an efficient iris recognition algorithm for recognizing the individual with high performance rateand accuracy. In this thesis, ten different methodologies namely DFT with SVM and KSVM algorithm (F-SVM andF-KSVM), Bernstein Polynomial with SVM and KSVM algorithm (B-SVM and B-KSVM), Hybrid DFT and Bernstein Polynomials with SVM and KSVM algorithm (FB-SVM and FB-KSVM), Hybrid DFT and Zernike momentswith SVM and KSVM algorithm (FZ-SVM and FZ-KSVM), Hybrid DFT, Bernstein Polynomials and Zernike Moments with SVM and KSVM algorithms(FBZ-SVM and 2FBZ-KSVM) have been proposed for recognizing the iris and areinvestigated using UBIRIS database. Initially, Singular Value Decomposition is used for removing the occlusions from the acquired input image. Once the noise present in the iris image iseliminated, Canny Edge Detection and Circular Hough Transform are used for segmenting the iris image from the pre-processed image. The proposed methodologies helps in extractingthe features from the segmented iris image which are further classified using machine learning algorithms such as Support Vector Machine and K-Means Support Vector Machine. The performance results are compared in terms of Accuracy, False Recognition Rate, False Acceptance Rate and Equal Error Rate. The Experiments results shows that the proposed algorithm provides better performance in terms of accuracy and recognition rates.