A novel method for low-constrained iris boundary localization

Iris recognition systems are strongly dependent on their segmentation processes, which have traditionally assumed rigid experimental constraints to achieve good performance, but now move towards less constrained environments. This work presents a novel method on iris segmentation that covers the localization of the pupillary and limbic iris boundaries. The method consists of an energy minimization procedure posed as a multilabel one-directional graph, followed by a model fitting process and the use of physiological priors. Accurate segmentations are achieved even in the presence of lutter, lenses, glasses, motion blur,and variable illumination. The contributions of this paper are a fast and reliable method for the accurate localizationof the iris boundaries in low-constrained conditions, and a novel database for iris segmentation incorporating challenging iris images, which has been publicly released to the research community. The proposed method has been evaluated over three different databases, showing higher performance in comparison to traditional techniques.Peer ReviewedPreprin

Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets

A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favorably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity, this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

Development of CUiris: A Dark-Skinned African Iris Dataset for Enhancement of Image Analysis and Robust Personal Recognition

Iris recognition algorithms, especially with the emergence of large-scale iris-based identification systems, must be tested for speed and accuracy and evaluated with a wide range of templates – large size, long-range, visible and different origins. This paper presents the acquisition of eye-iris images of dark-skinned subjects in Africa, a predominant case of verydark- brown iris images, under near-infrared illumination. The peculiarity of these iris images is highlighted from the histogram and normal probability distribution of their grayscale image entropy (GiE) values, in comparison to Asian and Caucasian iris images. The acquisition of eye-images for the African iris dataset is ongoing and will be made publiclyavailable as soon as it is sufficiently populated

Bio-Cryptosystem Using Fuzzy Vault Scheme

— In recent years most challenging problem is protection of information from unauthorized users. The conventional Cryptographic systems are insufficient to provide a security. The main problem is how to protect private keys from attackers and Intruder such as in case of Internet Banking. Cryptographic systems have been widely used in many information security systems. Hence in this paper we have proposed a framework of Biometric based cryptosystems. It provide reliable way of hiding private keys by using biometric features of individuals. A fuzzy vault approach is used to protect private keys and to release them only when legitimate individual enter their biometric sample. The main advantage of this system is there is no need of storing biometric information. However, fuzzy vault systems do not store directly these templates since they are encrypted with private keys by using novel cryptography algorithm. In proposed framework we are combining iris features with the encryption algorithm that can be a new research direction. The proposed approach provides high security and also image information can be protected. DOI: 10.17762/ijritcc2321-8169.150712