We present a large scale data set, OpenEDS: Open Eye Dataset, of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eyefacing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152 individual participants and is divided into four subsets: (i) 12,759 images with pixel-level annotations for key eye-regions: iris, pupil and sclera (ii) 252,690 unlabelled eye-images, (iii) 91,200 frames from randomly selected video sequence of 1.5 seconds in duration and (iv) 143 pairs of left and right point cloud data compiled from corneal topography of eye regions collected from a subset, 143 out of 152, participants in the study. A baseline experiment has been evaluated on OpenEDS for the task of semantic segmentation of pupil, iris, sclera and background, with the mean intersectionover-union (mIoU) of 98.3 %. We anticipate that OpenEDS will create opportunities to researchers in the eye tracking community and the broader machine learning and computer vision community to advance the state of eye-tracking for VR applications. The dataset is available for download upon request at this https URL

OpenEDS: Open Eye Dataset.

Segmentation and localization schemes for non-ideal iris biometric systems

Smartphones have become an important way to store sensitive information; therefore, users’ privacy needs to be highly protected. This can be done by using the most reliable and accurate biometric identification system available today: iris recognition. This paper develops and tests an iris recognition system for smartphones. The system uses eye images that rely on visible wavelength; these images are acquired by the smartphone built-in camera. The development of the system passes through four main phases: the first phase is the iris segmentation phase, which is done in three steps to detect the iris region from the captured image, which contains the eye and part of the face using Haar Cascade Classifier training, pupil localization, and iris localization using a Circular Hough Transform. In the second phase, the system applies normalization using a Rubber Sheet model, which converts the iris image to a fixed size pattern. In the third phase, unique features are extracted from that pattern using a Deep Sparse Filtering algorithm. Finally, in the matching phase, seven different matching techniques are investigated to decide the most appropriate one the system will use to verify the user. Two types of testing are conducted: Offline and Online tests. The BIPLab database and a collected dataset are used to measure the accuracy of the system phases and to calculate the Equal Error Rate (EER) for the whole system. The average EER is 0.18 for the BIPLab database and 0.26 for the collected dataset.

Developing Iris Recognition System for Smartphone Security

We present a large scale data set of eye-images captured using a virtual-reality (VR) head mounted display mounted with two synchronized eye-facing cameras at a frame rate of 200 Hz under controlled illumination. This dataset is compiled from video capture of the eye-region collected from 152 individual participants and is divided into four subsets: (i) 12,759 images with pixel-level annotations for key eye-regions: iris, pupil and sclera (ii) 252,690 unlabeled eye-images, (iii) 91,200 frames from randomly selected video sequences of 1.5 seconds in duration, and (iv) 143 pairs of left and right point cloud data compiled from corneal topography of eye regions collected from a subset, 143 out of 152, participants in the study. A baseline experiment has been evaluated on the dataset for the task of semantic segmentation of pupil, iris, sclera and background, with the mean intersection-over-union (mIoU) of 98.3 %. We anticipate that this dataset will create opportunities to researchers in the eye tracking community and the broader machine learning and computer vision community to advance the state of eye-tracking for VR applications, which in its turn will have greater implications in Human-Computer Interaction.

Dataset for Eye Tracking on a Virtual Reality Platform

The extraction of features involves the method of converting the original pixel values of an image to more meaningful, useful and measurable information that can be used in other techniques, such as image processing, pattern recognition and machine learning. The feature extraction plays a predominant role in iris recognition in which also the recognition rate is determined. The effective recognition accuracy, reduction of mis-classification of two iris templates mostly depends on feature extraction techniques. An efficient iris recognition system requires that the discriminating information presents in an iris pattern to be accurately obtained. This paper performed a literature review on different techniques of feature extraction of iris recognition. The recommendation was made on how these techniques can be further enhanced to produce an effective iris recognition system.

/pdf/feature-extraction-techniques-for-iris-recognition-system-a-56jcmxe1ax.pdf

Feature Extraction Techniques for Iris Recognition System: A Survey

The use of biometric systems has been increasingly encouraged by both government and private entities in order to replace or improve traditional security systems. Iris recognition biometric systems have proved to be efficient at personal recognition with highest recognition accuracy. This paper presents an up-to-date survey of iris segmentation algorithms that had been developed in literatures. It discusses the centrality of segmentation stage to effectiveness of iris recognition system. It identifies the methods used in the segmentation algorithms, database(s) on which the algorithms were used, the accuracy of the method and some noticed limitation(s) of the reviewed algorithms. Through this, it is able to identify the gaps for further researches in the field of application of iris recognition system in computer security. It was discovered that much work has not been done on black iris segmentation.Researchers are hereby encouraged to develop iris segmentation algorithm that can effectively localise black iris.

Iris Segmentation: a survey

Biometric as a technology has been proved to be a reliable means of enforcing constraint in a security sensitiveenvironment. Among the biometric technologies, iris recognition system is highly accurate and reliable becauseof their stable characteristics throughout lifetime. Iris recognition is one of the biometric identification thatemploys pattern recognition technology with the use of high resolution camera. Iris recognition consist of manysections among which feature extraction is an important stage. Extraction of iris features is very important andmust be successfully carried out before iris signature is stored as a template. This paper gives a comprehensivereview of different fundamental iris feature extraction methods, and some other methods available in literatures.It also gives a summarised form of performance accuracy of available algorithms. This establishes a platform onwhich future research on iris feature extraction algorithm(s) as a component of iris recognition system can bebased.Keywords: biometric authentication, false acceptance rate (FAR), false rejection rate (FRR), feature extraction,iris recognition system.

/pdf/iris-feature-extraction-a-survey-2xghk89agg.pdf

Iris feature extraction: a survey

This paper compared two Biometric Access Control Systems (BACS). The BACSs employed iris pattern for their authentication. Fast Fourier Transform-driven Access Control System (FACS) uses global iris features while Haar Wavelet Transform-driven Access Control System (HACS) uses local iris features for its template generation. Principal Component Analysis (PCA) was employed to select principal components of the extracted features (local and global). Fuzzy clustering was used for classification and Euclidean Distance (ED) as the distance metric. Experimental result showed that it took more time to train the HACS than FACS because of its intrinsic location in the iris images. It was discovered that global features driven Access Control System (FACS) with EER being 7.78 outperformed the local features driven Access Control System (HACS) with EER of 8.05. Though the two systems satisfied the benchmark of 80% for Recognition Accuracy (RA) of Biometric Systems, FACS exhibited RA of 89.87% while HACS achieved a RA of 83.83% when tested on iris images captured with CMITECH DMX-10 Portable USB 5.0 M pixel CCD Iris Camera at automatic convenient eye distances. Performance of global and local features on other biometric recognition systems can be tested and a means of combining the two features for hybridization can also be sought.

/pdf/comparative-analysis-of-two-biometric-access-control-systems-3r9m26tgp2.pdf

Comparative analysis of two biometric access control systems

The use of computer technology has significantly advanced the medical sector, and many computer technologies have been used to develop healthcare, such as the patient management system, monitoring and control systems, and diagnostic systems. Technological advances in healthcare have also helped in saving numerous patients and are constantly improving our quality of life. Technology in the medical sector has also had a major effect on almost all healthcare professional techniques and practices. In order to facilitate rapid diagnosis and treatment of different skin diseases by the use of a deep learning model, this study developed a comprehensive framework to improve the decision-making of dermatologists in Nigeria in terms of the diagnosis of selected skin diseases. The developed system achieved the network accuracy of 98.44 % and the validation accuracy of the test set is 99.44 % as specified by the training results, further testing reveal that the developed system yielded rejection rate of 2.2 % and recognition accuracy of 97.8 %.

https://jesr.ub.ro/1/article/download/287/268

An automated skin disease diagnostic system based on deep learning model

Necessity of containment of the spread of contagious virus cannot be overemphasized, COVID-19 inclusive. COVID-19 outbreak has shown how globally interconnected we are. Effects of its spread on individuals, communities, and nations in terms of trade and development are awesome and importance of prevention (containment) cannot be downplayed. Different methods employed in prediction of the spread of COVID-19 spread include linear regression model, machine learning technique, multiplicative calculus, Google trends, etc. Cost of prevention is much cheaper than containment and inherent prowess in soft computing paradigms, hence the design and implementation of predictive Fuzzy Inference System (FIS) model for the spread of COVID-19. The design employed Mandanin FIS for prediction of the spread of COVID-19 virus. Eight (8) COVID-19 symptoms were employed as inputs into the design which lead to three options at the output. The output are: “not infected”, “quarantine”, and “infected”. Triangular and trapezoidal membership functions were employed for fuzzification of inputs into the FIS while trapezoidal was employed at the output stage. The fuzzified 8 inputs were appropriately employed in formation of the fuzzy rule system of the FIS model. 128 group of fuzzy inference rule system was employed in the implementation. Performance of the FIS model revealed a great performance of the predictive system.

Fuzzy-Based Prediction of Spread of Covid-19 Pandemic

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