Hugo Proença

UBIRIS: a noisy iris image database

This paper presents a new iris database that contains images with noise. This is in contrast with the existing databases, that are noise free. UBIRIS is a tool for the development of robust iris recognition algorithms for biometric proposes. We present a detailed description of the many characteristics of UBIRIS and a comparison of several image segmentation approaches used in the current iris segmentation methods where it is evident their small tolerance to noisy images.

The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance

The iris is regarded as one of the most useful traits for biometric recognition and the dissemination of nationwide iris-based recognition systems is imminent. However, currently deployed systems rely on heavy imaging constraints to capture near infrared images with enough quality. Also, all of the publicly available iris image databases contain data correspondent to such imaging constraints and therefore are exclusively suitable to evaluate methods thought to operate on these type of environments. The main purpose of this paper is to announce the availability of the UBIRIS.v2 database, a multisession iris images database which singularly contains data captured in the visible wavelength, at-a-distance (between four and eight meters) and on on-the-move. This database is freely available for researchers concerned about visible wavelength iris recognition and will be useful in accessing the feasibility and specifying the constraints of this type of biometric recognition.

Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures

This paper focuses on noncooperative iris recognition, i.e., the capture of iris images at large distances, under less controlled lighting conditions, and without active participation of the subjects. This increases the probability of capturing very heterogeneous images (regarding focus, contrast, or brightness) and with several noise factors (iris obstructions and reflections). Current iris recognition systems are unable to deal with noisy data and substantially increase their error rates, especially the false rejections, in these conditions. We propose an iris classification method that divides the segmented and normalized iris image into six regions, makes an independent feature extraction and comparison for each region, and combines each of the dissimilarity values through a classification rule. Experiments show a substantial decrease, higher than 40 percent, of the false rejection rates in the recognition of noisy iris images

Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength

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.

Toward Covert Iris Biometric Recognition: Experimental Results From the NICE Contests

This paper announces and discusses the experimental results from the Noisy Iris Challenge Evaluation (NICE), an iris biometric evaluation initiative that received worldwide participation and whose main innovation is the use of heavily degraded data acquired in the visible wavelength and uncontrolled setups, with subjects moving and at widely varying distances. The NICE contest included two separate phases: 1) the NICE.I evaluated iris segmentation and noise detection techniques and 2) the NICE:II evaluated encoding and matching strategies for biometric signatures. Further, we give the performance values observed when fusing recognition methods at the score level, which was observed to outperform any isolated recognition strategy. These results provide an objective estimate of the potential of such recognition systems and should be regarded as reference values for further improvements of this technology, which-if successful-may significantly broaden the applicability of iris biometric systems to domains where the subjects cannot be expected to cooperate.

The NICE.I: Noisy Iris Challenge Evaluation - Part I

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).

Short communciation: Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage

Iris recognition has been widely used in several scenarios with very satisfactory results. As it is one of the earliest stages, the image segmentation is in the basis of the process and plays a crucial role in the success of the recognition task. In this paper we analyze the relationship between the accuracy of the iris segmentation process and the error rates of three typical iris recognition methods. We selected 5000 images of the UBIRIS, CASIA and ICE databases that the used segmentation algorithm can accurately segment and artificially simulated four types of segmentation inaccuracies. The obtained results allowed us to conclude about a strong relationship between translational segmentation inaccuracies - that lead to errors in phase - and the recognition error rates.

Periocular recognition: Analysis of performance degradation factors

Among the available biometric traits such as face, iris and fingerprint, there is an active research being carried out in the direction of unconstrained biometrics. Periocular recognition has proved its effectiveness and is regarded as complementary to iris recognition. The main objectives of this paper are three-fold: 1) to announce the availability of periocular dataset, which has a variability in terms of scale change (due to camera-subject distance), pose variation and non-uniform illumination; 2) to investigate the performance of periocular recognition methods with the presence of various degradation factors; 3) propose a new initialization strategy for the definition of the periocular region-of-interest (ROI), based on the geometric mean of eye corners. Our experiments confirm that performance can be consistently improved by this initialization method, when compared to the classical technique.

Quality Assessment of Degraded Iris Images Acquired in the Visible Wavelength

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.

Fast and globally convex multiphase active contours for brain MRI segmentation

Abstract Multiphase active contour based models are useful in identifying multiple regions with spatial consistency but varying characteristics such as the mean intensities of regions. Segmenting brain magnetic resonance images (MRIs) using a multiphase approach is useful to differentiate white and gray matter tissue for anatomical, functional and disease studies. Multiphase active contour methods are superior to other approaches due to their topological flexibility, accurate boundaries, robustness to image variations and adaptive energy functionals. Globally convex methods are furthermore initialization independent. We extend the relaxed globally convex Chan and Vese two-phase piecewise constant energy minimization formulation of Chan et al. (2006) [1] to the multiphase domain and prove the existence of a global minimizer in a specific space which is one of the novel contributions of the paper. An efficient dual minimization implementation of our binary partitioning function model accurately describes disjoint regions using stable segmentations by avoiding local minima solutions. Experimental results indicate that the proposed approach provides consistently better accuracy than other related multiphase active contour algorithms using four different error metrics (Dice, Rand Index, Global Consistency Error and Variation of Information) even under severe noise, intensity inhomogeneities, and partial volume effects in MRI imagery.Multiphase active contour based models are useful in identifying multiple regions with different characteristics such as the mean values of regions. This is relevant in brain magnetic resonance images (MRIs), allowing the differentiation of white matter against gray matter. We consider a well defined globally convex formulation of Vese and Chan multiphase active contour model for segmenting brain MRI images. A well-established theory and an efficient dual minimization scheme are thoroughly described which guarantees optimal solutions and provides stable segmentations. Moreover, under the dual minimization implementation our model perfectly describes disjoint regions by avoiding local minima solutions. Experimental results indicate that the proposed approach provides better accuracy than other related multiphase active contour algorithms even under severe noise, intensity inhomogeneities, and partial volume effects.