Cataract influence on iris recognition performance

Although human iris pattern is widely accepted as a stable biometric feature, recent research has found some evidences on the aging effect of iris system. In order to investigate changes in iris recognition performance due to the elapsed time between probe and gallery iris images, we examine the effect of elapsed time on iris recognition utilizing 7,628 iris images from 46 subjects with an average of ten visits acquired over two years from a legacy database at Clarkson University. Taken into consideration the impact of quality factors such as local contrast, illumination, blur and noise on iris recognition performance, regression models are built with and without quality metrics to evaluate the degradation of iris recognition performance based on time lapse factors. Our experimental results demonstrate the decrease of iris recognition performance along with increased elapsed time based on two iris recognition system (the modified Masek algorithm and a commercial software VeriEye SDK). These results also reveal the significance of quality factors in iris recognition regression indicating the variability in match scores. According to the regression analysis, our study in this paper helps provide the quantified decrease on match scores with increased elapsed time, which indicates the possibility to implement the prediction scheme for iris recognition performance based on learning of impact on time lapse factors.

This paper investigates the effect of segmentation errors on off-angle iris recognition. We first segment the inner and outer boundaries of off-angle iris images by fitting the best ellipses to boundaries. Then, we validate the segmentation results using Ground Truth Tool and fix the possible errors. Second, we add random errors on iris segmentation parameters to examine how Hamming distance distribution changes for different amount of segmentation error. In order to add errors, we first group the parameters into three sets as ellipse center (i.e., x, y), minor and major axis (i.e., r 1 , r 2 ) and orientation (θ) and changed the parameters by adding positive and negative random error as a noise to the ground truth segmentation parameters. Our purpose is to show how performance of iris recognition is affected if there is an error in segmentation and how important is to have a very robust segmentation algorithm for off-angle iris recognition. We test the effect of segmentation errors using our off-angle iris dataset that contains images from -50° to +50° in gaze angles. Based on our experimental results, we observed that error in ellipse center decrease the recognition performance worse than minor and major axis and orientation. The main reason is the normalization process tolerates the small amount of error in minor and major axis and bit shift in matching handles the small variations in the orientation errors. However, if the error in the ellipse orientation exceeds the limit of the bit shift method tolerance, the performance of the off-angle iris recognition dramatically drops.

Iris recognition systems are among the most accurate of all biometric technologies with immense potential for use in worldwide security applications. This study examined the effect of eye pathology on iris recognition and in particular whether eye disease could cause iris recognition systems to fail. The experiment involved a prospective cohort of 54 patients with anterior segment eye disease who were seen at the acute referral unit of the Princess Alexandra Eye Pavilion in Edinburgh. Iris camera images were obtained from patients before treatment was commenced and again at follow-up appointments after treatment had been given. The principal outcome measure was that of mathematical difference in the iris recognition templates obtained from patients' eyes before and after treatment of the eye disease. Results showed that the performance of iris recognition was remarkably resilient to most ophthalmic disease states, including corneal oedema, iridotomies (laser puncture of iris) and conjunctivitis. Problems were, however, encountered in some patients with acute inflammation of the iris (iritis/anterior uveitis). The effects of a subject developing anterior uveitis may cause current recognition systems to fail. Those developing and deploying iris recognition should be aware of the potential problems that this could cause to this key biometric technology.

Iris biometrics is considered as the unique and accurate biometric characteristics that are suited for large scale applications such as India's AADHAR, CANPASS, and many other national ID programs. However, the accuracy of the iris recognition is observed to degrade when eye (or iris) is affected by the diseases. Among many other eye diseases, cataract results in a cloud formation on the eye lens and is a potential problem, especially in the developing countries. In this paper, we present a preliminary study on 84 data subjects to reveal the effect of cataract on iris recognition performance. We investigate three different scenarios, which involve: (1) Enrolment and recognition of affected eyes (pre-operated eye) (2) Enrolement and recognition of operated eyes (post-operated eye) (3) Enrolment with affected eyes and recognition with operated eyes. Extensive experiments are carried out using five different academic and commercial iris recognition methods to get complete insight on the impact of cataract on the recognition performance. Results obtained on our database indicate the degraded performance in verification of the cataract operated eye.

In this paper, a self-adaptive method of iris boundary detection is presented and the method can segment the iris area accurately regardless of the shapes of iris boundaries. On the same time, a new feature extraction technique based on combination using special Gabor filters and wavelet maxima components is proposed. Finally, The radial basis function neural network (RBFNN) with a particle swarm optimization (PSO) a novel iris iris recognition technique with intelligent classifier is proposed for high performance iris recognition. this paper combines radial basis function neural network (RBFNN) and particle swarm optimization (PSO) for an optimized PNN classifier model. The experimental results reveal the proposed algorithm provides superior performance in iris recognition.

The traditional iris recognition systems require equal high quality human iris images. A cheap image acquisition system has difficulty in capturing equal high quality iris images. This paper describes a new feature representation method for iris recognition robust to noises. The disc-shaped iris image is first convolved with a low pass filter along the radial direction. Then, the radially smoothed iris image is decomposed in the angular direction using a one-dimensional continuous wavelet transform. Each decomposed one-dimensional waveform is approximated by an optimal piecewise linear curve connecting a small set of node points. The set of node points is used as a feature vector. The optimal approximation procedure reduces the feature vector size while maintaining recognition accuracy. The similarity between two iris images is measured by the normalized cross-correlation coefficients between optimal curves. The similarity between two iris images is estimated using mid-frequency bands. The rotation of one-dimensional signals due to the head tilt is estimated using the lowest frequency component. Experimentally we show the proposed method produces superb performance in iris recognition.

Cross-spectral iris recognition represents the ability of the system to identify the iris images acquired in different electromagnetic spectrums. An iris captured in the near-infrared spectrum (NIR) is matched with an iris obtained in the visual light spectrum (VIS) to boost the recognition performance. In cross-spectral iris recognition, the illumination factor between NIR and VIS images significantly degrades the recognition performance. Therefore, the existing method only achieved recognition performance with an equal error rate (EER) larger than 5%, and it is a challenging issue for cross-spectral performance to have EER below 5%. In this paper, we improve iris recognition performance by concatenating the Gradientfaces-based normalization technique (GRF) to a standard (conventional) iris recognition method to alleviate the illumination effect. In addition, we integrate the GRF with a Gabor filter, a difference of Gaussian (DoG) filter, and texture descriptors, namely a binary statistical image feature (BSIF) and a local binary pattern (LBP). The experimental results show that the GRF can boost the cross-spectral iris recognition performance with an EER equals to 1.69%. In addition, the best cross-spectral iris recognition performance is achieved when the GRF is integrated with the Gabor filter and the BSIF.

The role of biometrics, as an integral component of security infrastructures, has been firmly established in today's world. Its ability to accurately identify people from a distance has served as an important life-saving tool in the arsenalof our men and women in uniform, out in war theaters; it has also served as an effective forensics tool for our police force and off-late, is being increasingly adopted to secure sensitive transactions such as those with financial institutions.Traditionally, fingerprint patterns and facial photographs have held sway over identity veri fication tools; the former, because of its uniqueness and the latter because of its ubiquity. However, over the past decade, iris patterns have alsocome to the fore because of the combined advantage of uniqueness across the populace as well as the ability to image these patterns from a distance. Today most commercial iris acquisition systems report recognition accuracies upwards of 90%, over entire populations. However, the caveat is that these systems areconstrained to operate very close to the user, owing to the challenges involved in achieving sufficient resolution over the iride texture from large distances. This dissertation aims to push the boundaries of iris imaging devices and in-troduces theoretical and practical considerations in designing a novel long range iris recognition system. The aim is to design a system capable of acquiring enrollment quality iris images, similar in quality to those captured by short range systems such as IriShield, LG, PIER, HIIDE systems, at much larger stand-off distances. The challenges involved in acquiring images with sufficient spatialresolution, when subjects are too far out from the optimal acquisition range, are explored and potential solutions are described. First, we describe the design methodology behind creating a long range iris system, to aid the reader inunderstanding the key design considerations when using available commercial o -the-shelf hardware. Competing objectives such as achieving high magnifi cation while maintaining sufficient depth of field, using a sensor with large pixel pitch for low noise performance while resolving the required patterns, etc. are discussed. As a result of this work, the rst long range iris acquisition system of its kind, capable of acquiring enrollment iris images from up to 9mand matching up to 12m, has been designed and built. We show the system, present lessons learned during the design process as well as empirical results for the imaging performance. The latter half of the dissertation develops aniris texture enhancement algorithm for restoring degraded iris images - specifically noisy, low-resolution and out-of-focus/blurred images, which are often encountered in the real world. The method is developed and evaluated usingsynthesized degraded images from the NIST Iris Challenge Evaluation (ICE) database. A signifi cant improvement in verification performance is also seen, when using this enhancement method, on actual degraded images captured with the proposed long range iris system. The proposed enhancement method is modeled as a linear regression problem that exploits the sparsity of iris textures inan over-complete dictionary. Improvements in iris recognition performance using the enhanced textures are studied in the latter half of this dissertation. In summary, this work enhances and exceeds the capabilities of current iris imaging systems for the explicit purpose of accurately identifying threats using iride patterns, from distances larger than are currently possible. This will serve as anecessary tool in the arsenal of security establishments to identify threats, save lives and to protect our freedoms for generations to come.

Iris image suffer from noise due to non-cooperative situation. It creates challenge for segmentation. Here we propose deep learning based efficient segmentation method. It provides complete solution for such situation. To train this we have used various databases, which involved various illumination and imaging sensors. The proposed segmentation method can be utilized for all iris recognition methodology to improve performance in non-cooperative iris recognition. Key words: Iris Segmentation, multi task learning, iris localization, iris recognition

Smartphones are increasingly used as biometrie sensor for many authentication applications due to the computational ability and high resolution cameras that can be used to capture biometrie information. The objective of this paper is to assess the performance of iris versus periocular recognition for smartphones in non ideal conditions (change of illumination, highly pigmented iris, shadows on iris pattern) in real-life for verification in visible spectrum. We introduce various protocols for real-life verification scenarios using smartphones for iris and periocular recognition. Further, we also study the verification performance where enrollment and probe data originate from different smartphones. From the extensive set of experiments conducted on a publicly available smartphone database, it can be observed that the information from periocular region provides substantially good performance in terms of recognition accuracy in cross sensor and varying illumination scenarios as compared to iris under same conditions.

It is widely assumed that, barring traumatic injury to the eye or eye surgery, a person's iris does not change over time. This implies that iris recognition performance does not degrade as time increases after initial enrollment-also known as the template-aging problem. We present results of the first study to investigate the validity of this assumption. We explore the effects of time lapse since enrollment on iris biometric match scores using a data set with four years time lapse between the earliest and most recent images of an iris (23 subjects, 46 irises, 6,814 total images). Experimental results are reported for three iris recognition algorithms. We find that the empirical mean of the distribution for similarity scores between iris images of the same person changes over four years-in biometric parlance, the match or genuine distribution changes. The change is statistically significant when comparing the mean of pairs of images taken within 120 days (4 months) and over 1200 days (3.25 years) for the three algorithms in our study. The mean of the match distribution changes so that the expected performance would degrade due to an increase in the false reject rate. Our results suggest that iris biometric templates undergo aging.

Security assessment of partially encrypted visual data is known to be difficult. We show that iris recognition performance on encrypted sample data turns out to be a good predictor for protection strength of encryption schemes for visual data, in particular in settings where image structure and intelligibility is preserved to some extent. This is of particular interest, as common image quality metrics used for this task exhibit specifically poor prediction quality in this setting.

Security is an important issue in biometric recognition systems. In recent years, many researchers proposed to use watermarking to improve the security of biometric systems, but some people concern whether the embedded watermarks will influence recognition results. In this paper, we investigate the effects of several fragile and semi-fragile watermarking methods on the iris recognition performance. Experimental results demonstrate that, even images are fully embedded, fragile watermarking methods nearly have no effects on the recognition performance, while semi-fragile watermarking methods which embed watermark in the visually important components of images have larger effects on the recognition performance than the semi-fragile watermarking methods that embed watermark in the visually unimportant components of images. And embedding parameters, such as embedding strength and watermark length, also have some influences on the recognition results.

Most commercial iris recognition systems use infrared or near-infrared cameras to capture the iris images because they show rich and complex textures that can be used to identify a person. This paper presents the design and implementation of a multispectral iris acquisition system that can efficiently capture and store iris images exposed under different wavelengths of illumination. The goal is enable the study of wavelength effects on iris recognition performance. A set of multispectral illuminators is designed to sequentially activate for short periods of time to expose the iris to spectral bands in the 405 nm – 1550 nm range. A specialized camera is used to capture iris images for each wavelength. A software program was developed to control the illuminators, camera, and the image acquisition process.

In order to improve the performance of iris recognition, a novel method for iris recognition based on block theory and self-adaptive feature selection is proposed in this paper. Firstly, the normalized iris image is decomposed by convolving with multi-scale and multi-orientation Gabor filters, and then separated into several blocks, the block feature vector which includes mean and variance of Gabor coefficients inside each block can be obtained through statistical techniques, the iris feature vector of the whole iris image is then constructed by conjugating the block feature vector in row column order, finally the two-classifier of iris image are established based on the most distinguishable features, and the multi-classifiers of iris image are established by voting mechanism, and the performance is test by CASIA iris database. The results show that, compared with the traditional iris recognition methods, the proposed method has improved the iris recognition rate.