This paper delivers a new database of iris images collected in visible light using a mobile phone's camera and presents results of experiments involving existing commercial and open-source iris recognition methods, namely: Iri-Core, VeriEye, MIRLIN and OSIRIS. Several important observations are made. First, we manage to show that after simple preprocessing, such images offer good visibility of iris texture even in heavily-pigmented irides. Second, for all four methods, the enrollment stage is not much affected by the fact that different type of data is used as input. This translates to zero or close-to-zero Failure To Enroll, i.e., cases when templates could not be extracted from the samples. Third, we achieved good matching accuracy, with correct genuine match rate exceeding 94.5 for all four methods, while simultaneously being able to maintain zero false match rate in every case. Correct genuine match rate of over 99.5 was achieved using one of the commercial methods, showing that such images can be used with the existing biometric solutions with minimum additional effort required. Finally, the experiments revealed that incorrect image segmentation is the most prevalent cause of recognition accuracy decrease. To our best knowledge, this is the first database of iris images captured using a mobile device, in which image quality exceeds this of a near-infrared illuminated iris images, as defined in ISO IEC 19794-6 and 29794-6 documents. This database will be publicly available to all researchers.
@cite_14: The dramatic growth in practical applications for iris biometrics has been accompanied by relevant developments in the underlying algorithms and techniques. Among others, one active research area concerns about the development of iris recognition systems less constrained to users, either increasing the imaging distances, simplifying the acquisition protocols or the required lighting conditions. In this paper we address the possibility of perform reliable recognition using visible wavelength images captured under high heterogeneous lighting conditions, with subjects at-a-distance (between 4 and 8 meters) and on-the-move. The feasibility of this extremely ambitious type of recognition is analyzed, its major obstacles and challenges discussed and some directions for forthcoming work pointed.
@cite_9: Data quality assessment is a key issue, in order to broaden the applicability of iris biometrics to unconstrained imaging conditions. Previous research efforts sought to use visible wavelength (VW) light imagery to acquire data at significantly larger distances than usual and on moving subjects, which makes this real-world data notoriously different from the acquired in the near-infrared setup. Having empirically observed that published strategies to assess iris image quality do not handle the specificity of such data, this paper proposes a method to assess the quality of VW iris samples captured in unconstrained conditions, according to the factors that are known to determine the quality of iris biometric data: focus, motion, angle, occlusions, area, pupillary dilation, and levels of iris pigmentation. The key insight is to use the output of the segmentation phase in each assessment, which permits us to handle severely degraded samples that are likely to result of such imaging setup. Also, our experiments point that the given method improves the effectiveness of VW iris recognition, by avoiding that poor quality samples are considered in the recognition process.
@cite_6: Iris recognition imaging constraints are receiving increasing attention. There are several proposals to develop systems that operate in the visible wavelength and in less constrained environments. These imaging conditions engender acquired noisy artifacts that lead to severely degraded images, making iris segmentation a major issue. Having observed that existing iris segmentation methods tend to fail in these challenging conditions, we present a segmentation method that can handle degraded images acquired in less constrained conditions. We offer the following contributions: 1) to consider the sclera the most easily distinguishable part of the eye in degraded images, 2) to propose a new type of feature that measures the proportion of sclera in each direction and is fundamental in segmenting the iris, and 3) to run the entire procedure in deterministically linear time in respect to the size of the image, making the procedure suitable for real-time applications.
@cite_5: The human iris supports contact less data acquisition and can be imaged covertly. These factors give raise to the possibility of performing biometric recognition procedure with-out subjects’ knowledge and in uncontrolled data acquisition scenarios. The feasibility of this type of recognition has been receiving increasing attention, as is of particular interest in visual surveillance, computer forensics, threat assessment, and other security areas. In this paper we stress the role played by the spectrum of the visible light used in the acquisition process and assess the discriminating iris patterns that are likely to be acquired according to three factors: type of illuminant, it’s luminance, and levels of iris pigmentation. Our goal is to perceive and quantify the conditions that appear to enable the biometric recognition process with enough confidence.
@cite_10: This paper gives an overview of the NICE.I : Noisy Iris Challenge Evaluation -Part I contest. This contest differs from others in two fundamental points. First, instead of the complete iris recognition process, it exclusively evaluates the iris segmentation and noise detection stages, allowing the independent evaluation of one of the main recognition error sources. Second, it operates on highly noisy images that were captured to simulate less constrained imaging environments and constitute the second version of the UBIRIS database (UBIRIS.v2).
Upon the UBIRISv2 database, the NICE.I competition was founded by Proenca al @cite_10 , focusing on the noisy iris images and independent evaluation of segmentation and noise recognition stages, as those two are acknowledged by the authors to be the most likely source of errors. Following that, Proenca discusses challenges associated with unconstrained iris recognition in visible light @cite_14 , most notably the amount of information that can be captured using visible light acquisition setups. Santos al explore configurations of visible light illumination best for unconstrained iris recognition @cite_5 , such as the choice of illuminant and its luminance in respect to different eye pigmentation types. Proenca also proposes a new method for segmenting poor quality iris images captured in visible illumination @cite_6 , and methods for assessing image quality in order to improve an overall system performance by discarding samples of substandard quality @cite_9 .