In this paper we describe Iris recognition using Modified Fuzzy Hypersphere Neural Network (MFHSNN) with its learning algorithm, which is an extension of Fuzzy Hypersphere Neural Network (FHSNN) proposed by Kulkarni et al. We have evaluated performance of MFHSNN classifier using different distance measures. It is observed that Bhattacharyya distance is superior in terms of training and recall t...

A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition ...

Novel method for personal identification, based on adaptive size neighborhood entropy of iris images, was proposed. Through the process of segmentation, iris was extracted from other regions of the human eye, geometrically transformed and normalized. Entropy calculations performed for different neighborhood sizes allows simultaneous distinguishing of fine and global iris texture. Described meth...

The dramatic growth in practical applications for iris biometrics has been accompanied by many important developments in the underlying algorithms and techniques. Among others, one of the most active research areas concerns about the development of iris recognition systems less constrained to users, either increasing the image acquisition distances or the required lighting conditions. The main ...

Human users have a tough time remembering long cryptographic keys. Hence, researchers, for so long, have been examining ways to utilize biometric features of the user instead of a memorable password or passphrase, in an effort to generate strong and repeatable cryptographic keys. Our objective is to incorporate the volatility of the user's biometric features into the generated key, so as to mak...

Iris recognition is considered as one of the best biometric methods used for human identification and verification, this is because of its unique features that differ from one person to another, and its importance in the security field. This paper proposes an algorithm for iris recognition and classification using a system based on Local Binary Pattern and histogram properties as a statistical ...

This paper is related to the development of an innovative multimodal biometric identification system. Unimodal biometric systems often face significant limitations due to sensitivity to noise intraclass variability and other factors. Multimodal biometric identification systems aim to fuse two or more physical or behavioral traits to provide optimal False Acceptance Rate (FAR) and False Rejectio...

Iris is one of the most currently used biometric trait as it has random discriminating texture which does not change over a person’s lifetime. It is unique for all individuals, even for twins and the left and right eyes of the same individual. This paper describes an iris recognition system which includes phases like segmentation, normalization, segregating unwanted parts like occlusion, specul...

by Tanya H. Peters Every year we see a growing use of iris recognition, with it now utilized as a means of border control in a number of countries, including the United Kingdom, Canada, and the United Arab Emirates. As this technology becomes more common and more relied upon, the importance of algorithms that can identify subjects in a robust, consistent, and accurate manner becomes all-importa...

Abstract Recently, the Iris Recognition system has been considered an effective biometric model for recognizing humans. This paper introduces hybrid technique combining edge detection and segmentation, in addition to convolutional neural network (CNN) Hamming Distance (HD), extracting features classification. The proposed is applied different datasets, which are CASIA-Iris-Interval V4, IITD, MM...