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

Techniques for Ocular Biometric Recognition Under Non-ideal Conditions

The use of the ocular region as a biometric cue has gained considerable traction due to recent advances in automated iris recognition. However, a multitude of factors can negatively impact ocular recognition performance under unconstrained conditions (e.g., non-uniform illumination, occlusions, motion blur, image resolution, etc.). This dissertation develops techniques to perform iris and ocular recognition under challenging conditions. The first contribution is an image-level fusion scheme to improve iris recognition performance in low-resolution videos. Information fusion is facilitated by the use of Principal Components Transform (PCT), thereby requiring modest computational efforts. The proposed approach provides improved recognition accuracy when low-resolution iris images are compared against high-resolution iris images. The second contribution is a study demonstrating the effectiveness of the ocular region in improving face recognition under plastic surgery. A score-level fusion approach that combines information from the face and ocular regions is proposed. The proposed approach, unlike other previous methods in this application, is not learning-based, and has modest computational requirements while resulting in better recognition performance. The third contribution is a study on matching ocular regions extracted from RGB face images against that of near-infrared iris images. Face and iris images are typically acquired using sensors operating in visible and near-infrared wavelengths of light, respectively. To this end, a sparse representation approach which generates a joint dictionary from corresponding pairs of face and iris images is designed. The proposed joint dictionary approach is observed to outperform classical ocular recognition techniques. In summary, the techniques presented in this dissertation can be used to improve iris and ocular recognition in practical, unconstrained environments

Accurate Iris Localization Using Edge Map Generation and Adaptive Circular Hough Transform for Less Constrained Iris Images

This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIA-Iris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results

Robust iris recognition under unconstrained settings

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

A Survey on IRIS Recognition System: Comparative Study

Because of an increasing emphasis on security, Iris recognition has gained a great attention in both research and practical applications over the past decade. The demand for iris recognition in the various fields of access control reducing fraudulent transactions in electronic commences, security at border areas etc is increasing day by day due to its high accuracy, reliability and uniqueness. A review of various segmentation approaches used in iris recognition is done in this paper. The performance of the iris recognition systems depends heavily on segmentation and normalization techniques

Iris Recognition in Multiple Spectral Bands: From Visible to Short Wave Infrared

The human iris is traditionally imaged in Near Infrared (NIR) wavelengths (700nm-900nm) for iris recognition. The absorption co-efficient of color inducing pigment in iris, called Melanin, decreases after 700nm thus minimizing its effect when iris is imaged at wavelengths greater than 700nm. This thesis provides an overview and explores the efficacy of iris recognition at different wavelength bands ranging from visible spectrum (450nm-700nm) to NIR (700nm-900nm) and Short Wave Infrared (900nm-1600nm). Different matching methods are investigated at different wavelength bands to facilitate cross-spectral iris recognition.;The iris recognition analysis in visible wavelengths provides a baseline performance when iris is captured using common digital cameras. A novel blob-based matching algorithm is proposed to match RGB (visible spectrum) iris images. This technique generates a match score based on the similarity between blob like structures in the iris images. The matching performance of the blob based matching method is compared against that of classical \u27Iris Code\u27 matching method, SIFT-based matching method and simple correlation matching, and results indicate that the blob-based matching method performs reasonably well. Additional experiments on the datasets show that the iris images can be matched with higher confidence for light colored irides than dark colored irides in the visible spectrum.;As part of the analysis in the NIR spectrum, iris images captured in visible spectrum are matched against those captured in the NIR spectrum. Experimental results on the WVU multispectral dataset show promise in achieving a good recognition performance when the images are captured using the same sensor under the same illumination conditions and at the same resolution. A new proprietary \u27FaceIris\u27 dataset is used to investigate the ability to match iris images from a high resolution face image in visible spectrum against an iris image acquired in NIR spectrum. Matching in \u27FaceIris\u27 dataset presents a scenario where the two images to be matched are obtained by different sensors at different wavelengths, at different ambient illumination and at different resolution. Cross-spectral matching on the \u27FaceIris\u27 dataset presented a challenge to achieve good performance. Also, the effect of the choice of the radial and angular parameters of the normalized iris image on matching performance is presented. The experiments on WVU multispectral dataset resulted in good separation between genuine and impostor score distributions for cross-spectral matching which indicates that iris images in obtained in visible spectrum can be successfully matched against NIR iris images using \u27IrisCode\u27 method.;Iris is also analyzed in the Short Wave Infrared (SWIR) spectrum to study the feasibility of performing iris recognition at these wavelengths. An image acquisition setup was designed to capture the iris at 100nm interval spectral bands ranging from 950nm to 1650nm. Iris images are analyzed at these wavelengths and various observations regarding the brightness, contrast and textural content are discussed. Cross-spectral and intra-spectral matching was carried out on the samples collected from 25 subjects. Experimental results on this small dataset show the possibility of performing iris recognition in 950nm-1350nm wavelength range. Fusion of match scores from intra-spectral matching at different wavelength bands is shown to improve matching performance in the SWIR domain

Black hole algorithm along edge detector and circular hough transform based iris projection with biometric identification systems

The circular parameters between the pupil and the iris are found using current iris identification techniques but the accuracy creates an issue for the detection process during image processing. The procedure of extracting the iris region from an eye image using circular parameters can be improved via approximately too many approaches in literature but remain some portions under slightly unconstrained circumstances. In this study, we presented a Black Hole Algorithm (BHA) along the Canny edge detector and circular Hough transform-based optimization technique for circular parameter identification of iris segmentation. The iris boundary is discovered using the suggested segmentation approach and a computational model of the pixel value. The BHA looks for the central radius of the iris and pupil. The system uses MATLAB to test the CASIA-V3 database. The segmented images exhibit 98.71% accuracy. For all future access control applications, the segmentation-based BHA is effective at identifying the iris. The integration of the BHA with the Hough transforms and Canny edge detector is the main method by which the iris segmentation is accomplished. This novel technique improves the accuracy and effectiveness of iris segmentation, with potential uses in image analysis and biometric identification

Fast Iris Localization Based on Image Algebra and Morphological Operations

تحديد منطقة القزحية هي العملية الأكثر أهمية في نظام التعرف على القزحية التي تكون خاضعة وبشدة لتأثيرات البيئة,  وبالتالي، فقد تم اقتراح طريقة جديدة  لتحديد الحدود الداخلية والخارجية للقزحية. الفائدة الرئيسية من استخدام العمليات الحسابية للصور هي أنها طريقة بسيطة وسريعة وان هذه المميزات يتم استخدامها ودمجها مع العمليات المورفولوجية في تصميم الخوارزمية المقترحة. خوارزمية تحديد القزحية المقترحة قد صممت مع الأخذ بعين الاعتبار الملامح الشكلية لصورة قزحية العين مثل منطقة الضوضاء الموجودة في أجزاء مختلفة من صورة العين (مثل الانعكاسات الضوئية والتركيز والقزحية المرئية بصورة جزئية). النتائج التجريبية لتحديد القزحية تم إجراؤها على مجموعة من صور قزحية العين تتكون من 756 صورة تنتمي إلى قاعدة بيانات معهد العلوم الأكاديمي الصيني للأتمتة (CASIA V-1)، و450 صورة تنتمي إلى قاعدة بيانات جامعة الوسائط المتعددة (MMU V-1), تشير النتائج إلى تحقيق مستوى عالٍ من الدقة باستخدام التقنية المقترحة. علاوة على ذلك، فإن نتائج المقارنة مع خوارزميات تحديد القزحية الحديثة تعزز من دقة عملية فصل القزحية بشكل كبير اضافة الى كونها أكثر كفاءة من الناحية الحسابية.The localization of the iris is the most significant factor in biometrics of the iris, which traditionally assumes strictly controlled environments. The proposed method includes the pupillary and limbic iris boundaries localization. A primary advantage of image arithmetic operations is that the process is straightforward and therefore fast, these characteristics are employed and combined with the morphological operators in the designing of the proposed algorithm. The proposed algorithm takes into account the noise area which is found in various parts of the eye image such as light reflections, focus, and small visible iris. The experimental results are conducted on a collection of iris images consist of 756 images belong to Chinese Academy of Sciences Institute of Automation (CASIA V-1) and 450 images belong to Multi Media University (MMU V-1) databases.  The results indicate a high level of accuracy using the proposed technique. Moreover, the comparison results with the state-of-the-art iris localization algorithms expose considerable improvement in segmentation accuracy while being computationally more efficient