Feature-domain super-resolution framework for Gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics

Recognition of Human Iris Patterns

A biometric system of identification and authentication provides automatic recognition of an individual based on certain unique features or characteristics possessed by that individual. Iris recognition is a biometric identification method that uses pattern recognition on the images of the iris of an individual. Iris recognition is considered as one of the most accurate biometric methods available owing to the unique epigenetic patterns of the iris. In this project, we have developed a system that can recognize human iris patterns and an analysis of the results is done. A hybrid mechanism has been used for implementation of the system. Iris localization is done by amalgamating the Canny Edge Detection scheme and Circular Hough Transform. The iris images are then normalized so as to transform the iris region to have fixed dimensions in order to allow comparisons. Feature encoding has been used to extract the most discriminating features of the iris and is done using a modification of Gabor wavelets. And finally the biometric templates are compared using Hamming Distance which tells us whether the two iris images are same or not

Complex-valued Iris Recognition Network

In this work, we design a complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and amplitude information from the input iris texture in order to better represent its stochastic content. This necessitates the extraction and processing of phase information that cannot be effectively handled by a real-valued neural network. In this regard, we design a complex-valued neural network that can better capture the multi-scale, multi-resolution, and multi-orientation phase and amplitude features of the iris texture. We show a strong correspondence of the proposed complex-valued iris recognition network with Gabor wavelets that are used to generate the classical IrisCode; however, the proposed method enables automatic complex-valued feature learning that is tailored for iris recognition. Experiments conducted on three benchmark datasets - ND-CrossSensor-2013, CASIA-Iris-Thousand and UBIRIS.v2 - show the benefit of the proposed network for the task of iris recognition. Further, the generalization capability of the proposed network is demonstrated by training and testing it across different datasets. Finally, visualization schemes are used to convey the type of features being extracted by the complex-valued network in comparison to classical real-valued networks. The results of this work are likely to be applicable in other domains, where complex Gabor filters are used for texture modeling

Information theory and the iriscode

Iris recognition has legendary resistance to False Matches, and the tools of information theory can help to explain why. The concept of entropy is fundamental to understanding biometric collision avoidance. This paper analyses the bit sequences of IrisCodes computed both from real iris images and from synthetic “white noise” iris images whose pixel values are random and uncorrelated. The capacity of the IrisCode as a channel is found to be 0.566 bits per bit encoded, of which 0.469 bits of entropy per bit is encoded from natural iris images. The difference between these two rates reflects the existence of anatomical correlations within a natural iris, and the remaining gap from one full bit of entropy per bit encoded reflects the correlations in both phase and amplitude introduced by the Gabor wavelets underlying the IrisCode. A simple two-state Hidden Markov Model is shown to emulate exactly the statistics of bit sequences generated both from natural and white noise iris images, including their “imposter” distributions, and may be useful for generating large synthetic IrisCode databases.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/TIFS.2015.250019

Iris feature extraction: a survey

Biometric as a technology has been proved to be a reliable means of enforcing constraint in a security sensitiveenvironment. Among the biometric technologies, iris recognition system is highly accurate and reliable becauseof their stable characteristics throughout lifetime. Iris recognition is one of the biometric identification thatemploys pattern recognition technology with the use of high resolution camera. Iris recognition consist of manysections among which feature extraction is an important stage. Extraction of iris features is very important andmust be successfully carried out before iris signature is stored as a template. This paper gives a comprehensivereview of different fundamental iris feature extraction methods, and some other methods available in literatures.It also gives a summarised form of performance accuracy of available algorithms. This establishes a platform onwhich future research on iris feature extraction algorithm(s) as a component of iris recognition system can bebased.Keywords: biometric authentication, false acceptance rate (FAR), false rejection rate (FRR), feature extraction,iris recognition system