This paper presents the experimental study revealing weaker performance of the automatic iris recognition methods for cataract-affected eyes when compared to healthy eyes. There is little research on the topic, mostly incorporating scarce databases that are often deficient in images representing more than one illness. We built our own database, acquiring 1288 eye images of 37 patients of the Medical University of Warsaw. Those images represent several common ocular diseases, such as cataract, along with less ordinary conditions, such as iris pattern alterations derived from illness or eye trauma. Images were captured in near-infrared light (used in biometrics) and for selected cases also in visible light (used in ophthalmological diagnosis). Since cataract is a disorder that is most populated by samples in the database, in this paper we focus solely on this illness. To assess the extent of the performance deterioration we use three iris recognition methodologies (commercial and academic solutions) to calculate genuine match scores for healthy eyes and those influenced by cataract. Results show a significant degradation in iris recognition reliability manifesting by worsening the genuine scores in all three matchers used in this study (12 of genuine score increase for an academic matcher, up to 175 of genuine score increase obtained for an example commercial matcher). This increase in genuine scores affected the final false non-match rate in two matchers. To our best knowledge this is the only study of such kind that employs more than one iris matcher, and analyzes the iris image segmentation as a potential source of decreased reliability
As a possible cause of recognition failures for selected cases authors propose an explanation referencing to a combination of inner iris boundary's deviation from circularity due to posterior synechiae and excessive, pharmacologically induced pupil dilation. It is suggested that the proposed by Daugman @cite_14 that assumes linear, elastic change of iris pattern with changing pupil diameter is adequate enough when those changes are physiological, but insufficient when pupil dilation is pharmacologically induced. Other possible cause of degraded method’s performance might be affiliated with changes in the iris itself that are not visible during slit lamp examination, but reveal themselves when near infrared illumination is applied. In addition, posterior synechiae can disturb pupil circularity and thus create issues with image segmentation.
@cite_14
2118282683 2163155631 2014883954 121937170
Efficient and robust segmentation of less intrusively or non-cooperatively captured iris images is still a challenging task in iris biometrics. This paper proposes a novel two-stage algorithm for the localization and mapping of iris texture in images of the human eye into Daugman's doubly dimensionless polar coordinates. Motivated by the growing demand for real-time capable solutions, coarse center detection and fine boundary localization usually combined in traditional approaches are decoupled. Therefore, search space at each stage is reduced without having to stick to simpler models. Another motivation of this work is independence of sensors. A comparison of reference software on different datasets highlights the problem of database-specific optimizations in existing solutions. This paper instead proposes the application of Gaussian weighting functions to incorporate model-specific prior knowledge. An adaptive Hough transform is applied at multiple resolutions to estimate the approximate position of the iris center. Subsequent polar transform detects the first elliptic limbic or pupillary boundary, and an ellipsopolar transform finds the second boundary based on the outcome of the first. This way, both iris images with clear limbic (typical for visible-wavelength) and with clear pupillary boundaries (typical for near infrared) can be processed in a uniform manner. The richness and apparent stability of the iris texture make it a robust biometric trait for personal authentication. The performance of an automated iris recognition system is affected by the accuracy of the segmentation process used to localize the iris structure. Most segmentation models in the literature assume that the pupillary, limbic, and eyelid boundaries are circular or elliptical in shape. Hence, they focus on determining model parameters that best fit these hypotheses. However, it is difficult to segment iris images acquired under nonideal conditions using such conic models. In this paper, we describe a novel iris segmentation scheme employing geodesic active contours (GACs) to extract the iris from the surrounding structures. Since active contours can 1) assume any shape and 2) segment multiple objects simultaneously, they mitigate some of the concerns associated with traditional iris segmentation models. The proposed scheme elicits the iris texture in an iterative fashion and is guided by both local and global properties of the image. The matching accuracy of an iris recognition system is observed to improve upon application of the proposed segmentation algorithm. Experimental results on the CASIA v3.0 and WVU nonideal iris databases indicate the efficacy of the proposed technique. The primary objective of this paper is to propose a complete methodology for eye liveness detection based on pupil dynamics. This method may serve as a component of presentation attack detection in iris recognition systems, making them more secure. Due to a lack of public databases that would support this paper, we have built our own iris capture device to register pupil size changes under visible light stimuli, and registered 204 observations for 26 subjects (52 different irides), each containing 750 iris images taken every 40 ms. Each measurement registers the spontaneous pupil oscillations and its reaction after a sudden increase of the intensity of visible light. The Kohn and Clynes pupil dynamics model is used to describe these changes; hence we convert each observation into a feature space defined by model parameters. To answer the question whether the eye is alive (that is, if it reacts to light changes as a human eye) or the presentation is suspicious (that is, if it reacts oddly or no reaction is observed), we use linear and nonlinear support vector machines to classify natural reaction and spontaneous oscillations, simultaneously investigating the goodness of fit to reject bad modeling. Our experiments show that this approach can achieve a perfect performance for the data we have collected. All normal reactions are correctly differentiated from spontaneous oscillations. We investigated the shortest observation time required to model the pupil reaction, and found that time periods not exceeding 3 s are adequate to offer a perfect performance. This paper presents a multi-stage iris segmentation framework for the localization of pupillary and limbic boundaries of human eyes. Instead of applying time-consuming exhaustive search approaches, like traditional circular Hough Transform or Daugman's integrodifferential operator, an iterative approach is used. By decoupling coarse center detection and fine boundary localization, faster processing and modular design can be achieved. This alleviates more sophisticated quality control and feedback during the segmentation process. By avoiding database-specific optimizations, this work aims at supporting different sensors and light spectra, i.e. Visible Wavelength and Near Infrared, without parameter tuning. The system is evaluated by using multiple open iris databases and it is compared to existing classical approaches.