A robust sclera segmentation algorithm

Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm’s robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities’ space generated by the classifiers at stage 1. The proposed method was ranked the 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%

Iris localization by means of adaptive thresholding and Circular Hough Transform

In this paper, a new iris localization method for mobile devices is presented. Our system uses both intensity and saturation threshold on the captured eye images to determine iris boundary and sclera area, respectively. Estimated iris boundary pixels which have been placed outside the sclera will be removed. The remaining pixels are mainly the boundary of iris inside the sclera. Then, circular Hough transform is applied to such iris boundary pixels in order to localize the iris. Experiments were done on 60 iris images taken by a HTC mobile device from 10 different persons with both left and right eyes images available per person. Also, we evaluate the proposed algorithm on MICHE datasets include iphone5, Samsung Galaxy S4 and Samsung Galaxy Tab2. Experimental evaluation shows that the proposed system can successfully localize iris on tested images

Improving Iris Recognition through Quality and Interoperability Metrics

The ability to identify individuals based on their iris is known as iris recognition. Over the past decade iris recognition has garnered much attention because of its strong performance in comparison with other mainstream biometrics such as fingerprint and face recognition. Performance of iris recognition systems is driven by application scenario requirements. Standoff distance, subject cooperation, underlying optics, and illumination are a few examples of these requirements which dictate the nature of images an iris recognition system has to process. Traditional iris recognition systems, dubbed stop and stare , operate under highly constrained conditions. This ensures that the captured image is of sufficient quality so that the success of subsequent processing stages, segmentation, encoding, and matching are not compromised. When acquisition constraints are relaxed, such as for surveillance or iris on the move, the fidelity of subsequent processing steps lessens.;In this dissertation we propose a multi-faceted framework for mitigating the difficulties associated with non-ideal iris. We develop and investigate a comprehensive iris image quality metric that is predictive of iris matching performance. The metric is composed of photometric measures such as defocus, motion blur, and illumination, but also contains domain specific measures such as occlusion, and gaze angle. These measures are then combined through a fusion rule based on Dempster-Shafer theory. Related to iris segmentation, which is arguably one of the most important tasks in iris recognition, we develop metrics which are used to evaluate the precision of the pupil and iris boundaries. Furthermore, we illustrate three methods which take advantage of the proposed segmentation metrics for rectifying incorrect segmentation boundaries. Finally, we look at the issue of iris image interoperability and demonstrate that techniques from the field of hardware fingerprinting can be utilized to improve iris matching performance when images captured from distinct sensors are involved

Deep into the Eyes: Applying Machine Learning to improve Eye-Tracking

Eye-tracking has been an active research area with applications in personal and behav- ioral studies, medical diagnosis, virtual reality, and mixed reality applications. Improving the robustness, generalizability, accuracy, and precision of eye-trackers while maintaining privacy is crucial. Unfortunately, many existing low-cost portable commercial eye trackers suffer from signal artifacts and a low signal-to-noise ratio. These trackers are highly depen- dent on low-level features such as pupil edges or diffused bright spots in order to precisely localize the pupil and corneal reflection. As a result, they are not reliable for studying eye movements that require high precision, such as microsaccades, smooth pursuit, and ver- gence. Additionally, these methods suffer from reflective artifacts, occlusion of the pupil boundary by the eyelid and often require a manual update of person-dependent parame- ters to identify the pupil region. In this dissertation, I demonstrate (I) a new method to improve precision while maintaining the accuracy of head-fixed eye trackers by combin- ing velocity information from iris textures across frames with position information, (II) a generalized semantic segmentation framework for identifying eye regions with a further extension to identify ellipse fits on the pupil and iris, (III) a data-driven rendering pipeline to generate a temporally contiguous synthetic dataset for use in many eye-tracking ap- plications, and (IV) a novel strategy to preserve privacy in eye videos captured as part of the eye-tracking process. My work also provides the foundation for future research by addressing critical questions like the suitability of using synthetic datasets to improve eye-tracking performance in real-world applications, and ways to improve the precision of future commercial eye trackers with improved camera specifications

Unconstrained Iris Recognition

This research focuses on iris recognition, the most accurate form of biometric identification. The robustness of iris recognition comes from the unique characteristics of the human, and the permanency of the iris texture as it is stable over human life, and the environmental effects cannot easily alter its shape. In most iris recognition systems, ideal image acquisition conditions are assumed. These conditions include a near infrared (NIR) light source to reveal the clear iris texture as well as look and stare constraints and close distance from the capturing device. However, the recognition accuracy of the-state-of-the-art systems decreases significantly when these constraints are relaxed. Recent advances have proposed different methods to process iris images captured in unconstrained environments. While these methods improve the accuracy of the original iris recognition system, they still have segmentation and feature selection problems, which results in high FRR (False Rejection Rate) and FAR (False Acceptance Rate) or in recognition failure. In the first part of this thesis, a novel segmentation algorithm for detecting the limbus and pupillary boundaries of human iris images with a quality assessment process is proposed. The algorithm first searches over the HSV colour space to detect the local maxima sclera region as it is the most easily distinguishable part of the human eye. The parameters from this stage are then used for eye area detection, upper/lower eyelid isolation and for rotation angle correction. The second step is the iris image quality assessment process, as the iris images captured under unconstrained conditions have heterogeneous characteristics. In addition, the probability of getting a mis-segmented sclera portion around the outer ring of the iris is very high, especially in the presence of reflection caused by a visible wavelength light source. Therefore, quality assessment procedures are applied for the classification of images from the first step into seven different categories based on the average of their RGB colour intensity. An appropriate filter is applied based on the detected quality. In the third step, a binarization process is applied to the detected eye portion from the first step for detecting the iris outer ring based on a threshold value defined on the basis of image quality from the second step. Finally, for the pupil area segmentation, the method searches over the HSV colour space for local minima pixels, as the pupil contains the darkest pixels in the human eye. In the second part, a novel discriminating feature extraction and selection based on the Curvelet transform are introduced. Most of the state-of-the-art iris recognition systems use the textural features extracted from the iris images. While these fine tiny features are very robust when extracted from high resolution clear images captured at very close distances, they show major weaknesses when extracted from degraded images captured over long distances. The use of the Curvelet transform to extract 2D geometrical features (curves and edges) from the degraded iris images addresses the weakness of 1D texture features extracted by the classical methods based on textural analysis wavelet transform. Our experiments show significant improvements in the segmentation and recognition accuracy when compared to the-state-of-the-art results

Robust iris recognition under unconstrained settings

Tese de mestrado integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201