Pigment Melanin: Pattern for Iris Recognition

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on Instruments and Measurements, Volume 59, Issue number 4, April 201

Iris Recognition Using Scattering Transform and Textural Features

Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%

Dual iris authentication system using dezert smarandache theory

In this paper, a dual iris authentication using Dezert Smarandache theory is presented. The proposed method consists of three main steps: In the first one, the iris images are segmented in order to extract only half iris disc that contains relevant information and is less affected by noise. For that, a Hough transform is used. The segmented images are normalized by Daugman rubber sheet model. In the second step, the normalized images are analyzed by a bench of two 1D Log-Gabor filters to extract the texture characteristics. The encoding is realized with a phase of quantization developed by J. Daugman to generate the binary iris template. For the authentication and the similarity measurement between both binary irises templates, the hamming distances are used with a previously calculated threshold. The score fusion is applied using DSmC combination rule. The proposed method has been tested on a subset of iris database CASIA-IrisV3-Interval. The obtained results give a satisfactory performance with accuracy of 99.96%, FAR of 0%, FRR of 3.89%, EER of 2% and processing time for one iris image of 12.36 s

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

Feature Matching in Iris Recognition System using MATLAB

Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques.  In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software

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