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

Towards Accurate Pupil Detection Based on Morphology and Hough Transform

التعرف التلقائي على الأفراد مهم للغاية في العصور الحديثة. ظهرت تقنيات القياس الحيوي كإجابة على مسألة التعرف الفردي التلقائي. تميل هذه الورقة إلى إعطاء تقنية لاكتشاف البؤبؤ وهي مزيج من العمليات المورفولوجية السهلة ، و تحويل Hough (HT) . يتم تقسيم المنطقة الدائرية للعين والبؤبؤ بواسطة المرشح المورفولوجي وكذلك تحويل Hough حيث تم تحويل منطقة Iris القزحية المحلية إلى كتلة مستطيلة لغرض حساب التناقضات في الصورة. يتم تنفيذ هذه الطريقة واختبارها على قاعدة بيانات صور قزحية الأكاديمية الصينية للعلوم(CASIA V4)  لـ 249  شخص وقاعدة بيانات IIT Delhi (IITD) iris v1 باستخدام ماتلاب  MATLAB 2017a  . تتميز هذه الطريقة بدقة عالية في ايجاد المركز وتبلغ نسبة الوصول إلى دائرة نصف قطرها 97٪ لـ 2268 قزحية على صور CASIA V4 و 99.77٪ لصور قزحية 2240 على IITD، والسرعة مقبولة مقارنة بسرعة الكشف في الوقت الحقيقي والأداء المستقر. Automatic recognition of individuals is very important in modern eras. Biometric techniques have emerged as an answer to the matter of automatic individual recognition. This paper tends to give a technique to detect pupil which is a mixture of easy morphological operations and Hough Transform (HT) is presented in this paper. The circular area of the eye and pupil is divided by the morphological filter as well as the Hough Transform (HT) where the local Iris area has been converted into a rectangular block for the purpose of calculating inconsistencies in the image. This method is implemented and tested on the Chinese Academy of Sciences (CASIA V4) iris image database 249 person and the IIT Delhi (IITD) iris database v1 using MATLAB 2017a. This method has high accuracy in the center and radius finding reaches 97% for 2268 iris on CASIA V4 image and 99.77% for 2240 iris images on IITD, the speed is acceptable compared to the real-time detection speed and stable performance

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

Feature Matching in Iris Recognition System using MATLAB

Iris recognition system is a secure human authentication in biometric technology. Iris recognition system consists of five stages. They are Feature matching, Feature encoding, Iris Normalization, Iris Segmentation and Image acquisition. In Image acquisition, the eye Image is captured from the CASIA database, the Image must have good quality with high resolution to process next steps. In Iris Segmentation, the Iris part is detected by using Hough transform technique and Canny Edge detection technique. Iris from an eye Image segmented. In normalization, the Iris region is converted from the circular region into a rectangular region by using polar transform technique. In feature encoding, the normalized Iris can be encoded in the form of binary bit format by using Gabor filter techniques.  In feature matching, the encoded Iris template is compared with database eye Image of Iris template and generated the matching score by using Hamming distance technique and Euclidean distance technique. Based on the matching score, we get the result. This project is developed using Image processing toolbox of Matlab software

Enhanced iris recognition: Algorithms for segmentation, matching and synthesis

This thesis addresses the issues of segmentation, matching, fusion and synthesis in the context of irises and makes a four-fold contribution. The first contribution of this thesis is a post matching algorithm that observes the structure of the differences in feature templates to enhance recognition accuracy. The significance of the scheme is its robustness to inaccuracies in the iris segmentation process. Experimental results on the CASIA database indicate the efficacy of the proposed technique. The second contribution of this thesis is a novel iris segmentation scheme that employs Geodesic Active Contours to extract the iris from the surrounding structures. The proposed scheme elicits the iris texture in an iterative fashion depending upon both the local and global conditions of the image. The performance of an iris recognition algorithm on both the WVU non-ideal and CASIA iris database is observed to improve upon application of the proposed segmentation algorithm. The third contribution of this thesis is the fusion of multiple instances of the same iris and multiple iris units of the eye, i.e., the left and right iris at the match score level. Using simple sum rule, it is demonstrated that both multi-instance and multi-unit fusion of iris can lead to a significant improvement in matching accuracy. The final contribution is a technique to create a large database of digital renditions of iris images that can be used to evaluate the performance of iris recognition algorithms. This scheme is implemented in two stages. In the first stage, a Markov Random Field model is used to generate a background texture representing the global iris appearance. In the next stage a variety of iris features, viz., radial and concentric furrows, collarette and crypts, are generated and embedded in the texture field. Experimental results confirm the validity of the synthetic irises generated using this technique

Pupil Center Detection Approaches: A comparative analysis

In the last decade, the development of technologies and tools for eye tracking has been a constantly growing area. Detecting the center of the pupil, using image processing techniques, has been an essential step in this process. A large number of techniques have been proposed for pupil center detection using both traditional image processing and machine learning-based methods. Despite the large number of methods proposed, no comparative work on their performance was found, using the same images and performance metrics. In this work, we aim at comparing four of the most frequently cited traditional methods for pupil center detection in terms of accuracy, robustness, and computational cost. These methods are based on the circular Hough transform, ellipse fitting, Daugman's integro-differential operator and radial symmetry transform. The comparative analysis was performed with 800 infrared images from the CASIA-IrisV3 and CASIA-IrisV4 databases containing various types of disturbances. The best performance was obtained by the method based on the radial symmetry transform with an accuracy and average robustness higher than 94%. The shortest processing time, obtained with the ellipse fitting method, was 0.06 s.Comment: 15 pages, 9 figures, submitted to the journal "Computaci\'on y Sistemas