Iris Texture Patterns Research Articles

Phacoemulsification Cataract Surgery Affects the Discriminative Capacity of Iris Pattern Recognition

Cataract is a common ophthalmic disorder and the leading cause of blindness worldwide. While cataract is cured via surgical procedures, its impact on iris based biometric recognition has not been effectively studied. The key objective of this research is to assess the effect of cataract surgery on the iris texture pattern as a means of personal authentication. We prepare and release the IIITD Cataract Surgery Database (CaSD) captured from 132 cataract patients using three commercial iris sensors. A non-comparative non-randomized cohort study is performed on the iris texture patterns in CaSD and authentication performance is studied using three biometric recognition systems. Performance is lower when matching pre-operative images to post-operative images (74.69 ± 9.77%) as compared to matching pre-operative images to pre-operative images (93.42 ± 1.76%). 100% recognition performance is observed on a control-group of healthy irises from 68 subjects. Authentication performance improves if cataract affected subjects are re-enrolled in the system, though re-enrollment does not ensure performance at par with pre-operative scenarios (86.67 ± 5.64%). The results indicate that cataract surgery affects the discriminative nature of the iris texture pattern. This finding raises concerns about the reliability of iris-based biometric recognition systems in the context of subjects undergoing cataract surgery.

Cross-sensor iris spoofing detection using orthogonal features

Iris recognition systems face issues related to spoofing attacks that obfuscate the natural iris texture pattern. This makes it a challenging problem to be used for large-scale high security applications. The paper introduces a rotation-invariant feature-set comprising of Zernike moments and Polar harmonic transforms that extract local intensity variations for detection of iris spoofing attacks. Experimental results have been implemented on four publicly available iris spoofing databases: IIITD Contact Lens, IIITD Iris Spoofing, Clarkson LivDet-Iris 2015 and Warsaw LivDet-Iris 2015 that include both contact lens and print attack spoofing samples. Experiments demonstrate that the proposed system easily detects spoofing attacks even when acquired using multiple sensors.

An Effective Feature Extraction Approach for Iris Recognition System

Background/Objectives: Iris recognition system refers to a system used for identifying different iris texture patterns for different applications. The research is aimed at developing a system that improves the efficiency of the iris recognition system and make it more reliable and robust. Methods: To this end, we have developed a system based on compound local binary pattern technique. Compound local binary technique is a spatial domain technique, which assign a 2P bit code to central pixel based on the local neighbourhood comprising of P neighbours. The operator takes into consideration both the sign and magnitude information of the central and corresponding neighbour grey values. The unique and abundant features extracted through Compound Local Binary Pattern (CLBP) operator act as input to the neural network classifier. Findings: The system has been tested over 50 eye images taken from CASIA database. Iris recognition system based on Compound local binary pattern technique along with neural network used as classifier improves the accuracy of system in comparison to existing feature extraction approaches. In this proposed research, recognition rate achieved is 96%.

Automated Iris Recognition System: An Overview

Iris texture pattern can be used for biometric verification and identification of a person from a large dataset. Iris recognition is used in several fields, like border, security prone, industrial and medical institutes etc. Due to its high correctness and uniqueness, it is used in several fields of access control and border area security. The demand for iris recognition is increasing continuously due to its reliability, accuracy and uniqueness. To improve the overall recognition rate and performance of iris recognition system, the researchers have to work in different aspects like unconstrained environment, noisy images as well as blurred images. This paper overviews the different steps involve into iris recognition system as well as various methodology presents in the iris recognition system.

A Robust Approach in Iris Recognition for Person Authentication

In Iris recognition authentication process, iris and sclera are used as the previous inputs using to recognize the eye with different mechanisms like segmentation combining with different versions. In this paper, entirely biometric-based personal verification and identification methods have gained much interest with an increasing accent on safety. The iris texture pattern has no links with the genetic structure of an individual and since it is generated by chaotic processes externally visible patterns imaged from a distance. Iris patterns possess a high degree of randomness and uniqueness. Here we propose two algorithms they are K-Means algorithm and canny Edge Detection. Totally eight process acting to identify the pupil and also for iris recognition. Comparing images k- means algorithm is to give accurate contest. As a final point it leads to open authentication person details from database.

IRIS RECOGNITION METHOD BASED ON GABOR FILTERS AND UNIFORM LOCAL BINARY PATTERNS

Iris recognition has been recently given greater attention in human identification and it is becoming increasingly an active topic in research. This paper presents a novel iris recognition method based on multi-channel Gabor filtering and uniform local binary patterns (ULBP). First, the eye image is processed in order to obtain a segmented and normalized eye image by applying Hough transform and polar transformation. Second, the iris image is analyzed by Gabor filters to extract the global features of texture details. Then, ULBP operators are applied in each transformed image to describe the local arrangement of iris texture patterns. Next, the obtained representation is partitioned in blocks. Finally, we have encoded the local relationships between statistical measures computed in blocks to form a template of 240 bytes. We estimate the similarity between irises by computing the modified Hamming distance between templates. Tests were carried out on CASIA v3 iris database. Experimental results illustrate the effectiveness and robustness of ULBP to extract rich local and global information of iris texture when combined with simultaneously multi-blocks and multi-channel method. The comparative evaluations illustrate the good discriminative properties of extracted features for iris recognition.