Analysis of the effects of higher bits-per-cell iris recognition

In this paper, a modification of the iris recognition approach by Daugman (2004) is presented. Traditionally, each cell in an iris template consists of a matrix of two-bit values, extracted from a normalized iris image. An existing iris recognition system was modified, in order to allow for extraction of an arbitrary amount of bits per cell. This paper explains how the original iris recognition system works and how it was modified. Also, a statistical analysis of the impact on recognition quality is presented, which turned out to be limited

A Comparative Study of Different Template Matching Techniques for Twin Iris Recognition

Biometric recognition is gaining attention as most of the organization is seeking for a more secure verification method for user access and other security application. There are a lot of biometric systems that exist which are iris, hand geometry and fingerprint recognition. In biometric system, iris recognition is marked as one of the most reliable and accurate biometric in term of identification. However, the performance of iris recognition is still doubted whether the iris recognition can generate higher accuracy when involving twin iris data. So, specific research by using twin data only needs to be done to measure the performance of recognition. Besides that, a comparative study is carried out using two template matching technique which are Hamming Distance and Euclidean Distance to measure the dissimilarity between the two iris template. From the comparison of the technique, better template matching technique also can be determined. The experimental result showed that iris recognition can distinguish twin as it can distinguish two different, unrelated people as the result obtained showed the good separation between intra and interclass and both techniques managed to obtain high accuracy. From the comparison of template matching technique, Hamming Distance is chosen as better technique with low False Rejection Rate, low False Acceptance Rate and high Total Success Rate with the value of 2.5%, 8.75% and 96.48% respectively

Iris Recognition: Robust Processing, Synthesis, Performance Evaluation and Applications

The popularity of iris biometric has grown considerably over the past few years. It has resulted in the development of a large number of new iris processing and encoding algorithms. In this dissertation, we will discuss the following aspects of the iris recognition problem: iris image acquisition, iris quality, iris segmentation, iris encoding, performance enhancement and two novel applications.;The specific claimed novelties of this dissertation include: (1) a method to generate a large scale realistic database of iris images; (2) a crosspectral iris matching method for comparison of images in color range against images in Near-Infrared (NIR) range; (3) a method to evaluate iris image and video quality; (4) a robust quality-based iris segmentation method; (5) several approaches to enhance recognition performance and security of traditional iris encoding techniques; (6) a method to increase iris capture volume for acquisition of iris on the move from a distance and (7) a method to improve performance of biometric systems due to available soft data in the form of links and connections in a relevant social network

Iris Recognition System for Biometric Recognition

Volume 7 Issue 9 (September 201

Evaluation of Biometric System - Eye Iris Technology

Tato práce stručně popisuje základní pojmy z oblasti biometrie, historii biometrie, biometrický systém a jeho vlastnosti. Dále pojednává o základních principech technologie rozpoznávání podle duhovky oka. Také jsou popsány testy provedené na několika různých systémech rozpoznávání podle duhovky oka a nakonec jsou shrnuty dosažené výsledky.This thesis briefly describes basic biometric terms, a brief history of biometrics, biometric system and its performance and characteristics. Basic principles of iris recognition technology are also discussed. Finally, this thesis describes tests performed on various iris recognition systems and sums up the results.

Enhanced iris recognition: Algorithms for segmentation, matching and synthesis

This thesis addresses the issues of segmentation, matching, fusion and synthesis in the context of irises and makes a four-fold contribution. The first contribution of this thesis is a post matching algorithm that observes the structure of the differences in feature templates to enhance recognition accuracy. The significance of the scheme is its robustness to inaccuracies in the iris segmentation process. Experimental results on the CASIA database indicate the efficacy of the proposed technique. The second contribution of this thesis is a novel iris segmentation scheme that employs Geodesic Active Contours to extract the iris from the surrounding structures. The proposed scheme elicits the iris texture in an iterative fashion depending upon both the local and global conditions of the image. The performance of an iris recognition algorithm on both the WVU non-ideal and CASIA iris database is observed to improve upon application of the proposed segmentation algorithm. The third contribution of this thesis is the fusion of multiple instances of the same iris and multiple iris units of the eye, i.e., the left and right iris at the match score level. Using simple sum rule, it is demonstrated that both multi-instance and multi-unit fusion of iris can lead to a significant improvement in matching accuracy. The final contribution is a technique to create a large database of digital renditions of iris images that can be used to evaluate the performance of iris recognition algorithms. This scheme is implemented in two stages. In the first stage, a Markov Random Field model is used to generate a background texture representing the global iris appearance. In the next stage a variety of iris features, viz., radial and concentric furrows, collarette and crypts, are generated and embedded in the texture field. Experimental results confirm the validity of the synthetic irises generated using this technique

On fuzzy syndrome hashing with LDPC coding

The last decades have seen a growing interest in hash functions that allow some sort of tolerance, e.g. for the purpose of biometric authentication. Among these, the syndrome fuzzy hashing construction allows to securely store biometric data and to perform user authentication without the need of sharing any secret key. This paper analyzes this model, showing that it offers a suitable protection against information leakage and several advantages with respect to similar solutions, such as the fuzzy commitment scheme. Furthermore, the design and characterization of LDPC codes to be used for this purpose is addressed.Comment: in Proceedings 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL), ACM 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistributio