Yadav, Dr. RaghavJAURO, SULEIMAN SALIHU2020-02-172020-02-172020http://krishikosh.egranth.ac.in/handle/1/5810143105Ph. D. ThesisThese days, Iris Recognition (IR) is a technique of biometric verification of the individual authentication process centered upon the human iris unique pattern that is implemented to control system intended for high security. The recognition of a person centered on iris pattern is attaining more fame owing to the uniqueness of the pattern amongst the people. Iris recognition system’s performance relies upon the method of segmentation and classification of iris from the eye image. Segmentation and classification of the iris region are the most controversial issues in iris recognition system because the poor result of these stages can shatter or spoil the effectiveness of iris recognition systems.An Enhanced AMF algorithm was proposed to effectively eradicate noise while at the same time preserves the image detail. During noise detection a new variable was introduced to reflect the correlation betwixt pixel values. The filtering algorithms were applied under noise ratio (PNR) of 30%. Experimental outcomes on CASIA database reveals that with SNP of 30%, PSNR values of 14.1%, 19.58 and 23.42% were attained for CMF, AMF and proposed enhanced AMF filters respectively. The proposed ‘two-level segmentation’ methodology starts by converting the iris images picked from database from RGB to Gray scale images and then normalized using Daugman’s rubber sheet model. The interior and exterior boundaries are then detected from the denoisedimage.In interior boundary segmentation (IBS) section, the image was segmented utilizing some methods like Gaussian pyramid, anisotropic diffusion,thresholding, centroid computing, polar transform, and radius computing.Exterior boundary segmentation (EBS) section performs zigzag collarette process.Finally, the IBS was subtracted from EBS; to give the segmented iris. Three databases namely CASIA, UBRIS and MMU1.0 were utilized for testing the proposed approach. The proposed method reveals an accuracy of 97.33%, 97.97% and 97% in CASIA, UBIRIS and MMU databases respectively .ExperimentalresultswhencomparedwithACWOEandK-meansdemonstrated the superiority of the proposed technique. The proposed iris recognition framework takes the circle based iris segmented images as the input. Then, the Log Gabor, GLAC, Contour-let transform, LGXP, and Canny Edge Detection features are extracted. Thirdly, the extorted features are specified to the ANFIS for achieving the training process. Amid the training process, the ANFIS parameters are simultaneously optimized by the ACFO. The optimized parameters in ANFIS by ACFO efficiently perform the IR process. At last, the recognized iris images are found. The experiment outcomes for the proposed IR technique demonstrates its superiority over latest methodologies pertaining to precision (96% for CASIA, 99% for UBRIS and 96.19% for MMU database), Recall (100% for all databases), F-Measure (97.9% for CASIA, 98% for UBRIS and 99% for MMU database) together with Accuracy (98.6% for CASIA, 97.9% for UBRIS and 98% for MMU database).ennullThesis