Framework for biometric iris recognition in video, by deep learning and quality assessment of the iris-pupil region

In the current world scenario the influence of the COVID19 pandemic has reached universal proportions affecting almost all countries. In this sense, the need has arisen to wear gloves or to reduce direct contact with objects (such as sensors for capturing fingerprints or palm prints) as a sanitary measure to protect against the virus. In this new reality, it is necessary to have a biometric identification method that allows safe and rapid recognition of people at borders, or in quarantine controls, or in access to places of high biological risk, among others. In this scenario, iris biometric recognition has reached increasing relevance. This biometric modality avoids all the aforementioned inconveniences with proven high efficiency. However, there are still problems associated with the iris capturing and segmentation in real time that could affect the effectiveness of a System of this nature and that it is necessary to take into account. This work presents a framework for real time iris detection and segmentation in video as part of a biometric recognition system. Our proposal focuses on the stages of image capture, iris detection and segmentation in RGB video frames under controlled conditions (conditions of border and access controls, where people collaborate in the recognition process). The proposed framework is based on the direct detection of the iris-pupil region using the YOLO network, the evaluation of its quality and the semantic segmentation of iris by a Fully Convolutional Network. (FCN). The proposal of an evaluation step of the quality of the iris-pupil region reduce the passage to the system of images with problems of out of focus, blurring, occlusions, light changing and pose of the subject. For the evaluation of image quality, we propose a measure that combines parameters defined in ISO/IEC 19794-6 2005 and others derived from the systematization of the knowledge of the specialized literature. The experiments carried out in four different reference databases and an own video data set demonstrates the feasibility of its application under controlled conditions of border and access controls. The achieved results exceed or equal state-of-the-art methods under these working conditions.

Bilateral Acute Iris Transillumination without Prior Systemic Antibiotics.

Purpose: To describe a case of bilateral acute iris transillumination (BAIT) with no history of systemic infections or antibiotics intake before the attack. Methods: This study included the review of the clinical record of the patient. Results: A 29-year-old male was referred to the glaucoma clinic with presumed bilateral acute iridocyclitis with refractory glaucoma. Ophthalmic examination revealed bilateral pigment dispersion, marked iris transillumination, dense pigment deposition in the iridocorneal angle, and high intraocular pressure. The patient was diagnosed with BAIT and was followed for 5 months. Conclusion: The diagnosis of BAIT can be elicited even without a prior history of systemic infection or antibiotics intake.

Tuberculosis and Hiv Infection - So Many Questions Pending

Since the early days of HIV infection, back in the eighties, TB - particularly extrapulmonary TB emerged as one of the opportunistic infections affecting these patients, specifically as a reactivation of latent TB infections. A diagnosis of TB in the context of HIV infection was then considered as an 'AIDS defining condition' according to classification systems used at that time. It has been recognized for a long time that there are many interactions between HIV and Mycobacterium tuberculosis, which lead to further immune deterioration and to worsening of both conditions due to complex biological and mechanistic interactions between these two agents. Many methods and techniques have been proposed in order to improve diagnosis of TB in HIV-infected subjects, knowing that TB is the most frequent opportunistic infection;and, if not treated in a timely fashion, it may easily take the lives of affected patients. It is not easy to have a diagnosis of TB in HIV-infected subjects, because of the difficulties for obtaining adequate sputum samples, or because of lack of adequate facilities for making a timely diagnosis, particularly in the so-called developing world. On the other hand, extrapulmonary TB is most frequently found in HIV-infected individuals compared to non-infected subjects, and its diagnosis poses significant difficulties, since so many times invasive procedures must be performed in order to obtain an adequate tissue sample and then be able to identify the pathological characteristics of tuberculous disease. In the first days of HIV infection when no antiretroviral therapy was available, a diagnosis of TB was made on clinical grounds, considering a history of contact or some characteristics of the disease, and those of us who are old (or experienced) enough offered antituberculosis therapy for these subjects, obtaining an adequate response many times, but in all cases, the natural history of HIV infection took place, and ultimately these patients died because of the occurrence of another opportunistic infection (or malignancy). With the advent of antiretroviral therapy in the late nineties, another problem occurred. The possibility of drug-drug interactions, taking into account hepatic metabolism of rifampin and the alterations of antiretroviral drug blood - or tissue - concentrations. On top of this, the occurrence of IRIS became another problem, and strategies and protocols have been designed in order to establish the adequate timing of antituberculosis therapy and sometime later antiretroviral therapy. A last point to be considered is the COVID-19 pandemic. The question to be asked is what the influence of the pandemic has been for affecting TB and HIV diagnosis and therapy strategies and programs, particularly in the developing world, knowing that health systems in these countries have many limitations, and that - scant - resources had to be dedicated for the fight against the pandemic.Copyright © 2023

Technology-Driven Implementation of Smart Entrances in Public Places during the COVID-19

This research proposes a smart entrance system to cope with the COVID-19 pandemic in public places. The system can help automate standard operating procedures (SOPs) for checking. The paper focuses on exploring the problem context related to the COVID-19 SOPs for public places. The research on technologies involves using thermal cameras, fingerprint recognition, face recognition, iris recognition, object detection and cloud computing. These technologies can be integrated to provide a more versatile and effective solution. The technological solutions proposed by contemporary researchers are also critically analysed by investigating their advantages and disadvantages. © 2023 IEEE.

Bedside naso-jejunal placement is more difficult, but successful in patients with COVID-19 in critical care: A retrospective service evaluation of a dietitian-led service

The COVID-19 pandemic presented clinical and logistical challenges in the delivery of adequate nutrition in the critical care setting. The use of neuromuscular-blocking drugs, presence of maxilla-facial oedema, strict infection control procedures, and patients placed in a prone position complicated feeding tube placement. We audited the outcomes of dietitian-led naso-jejunal tube (NJT) insertions using the IRIS (Kangaroo, USA) device, before and during the COVID-19 pandemic. NJT placement was successful in 78% of all cases (n = 50), and 87% of COVID-19 cases. Anaesthetic support was only required in COVID-19 patients (53%). NJT placement using IRIS was more difficult but achievable in patients with COVID-19.Copyright © The Intensive Care Society 2023.

Machine learning-based iris liveness identification using fragmental energy of cosine transformed iris images

Iris biometric identification provides a contactless authentication preventing the spread of COVID-19 like diseases. These systems are made vulnerable and unsafe because of the spoofing attacks attempted with the help of contact lenses, video replays and print attacks. The paper proposes the iris liveness detection method to mitigate spoofing attacks, taking fragmental coefficients of cosine transformed iris image to be used as features. Seven variants of feature formation are considered in experimental validations of the proposed method, and the features are used to train eight assorted machine learning classifiers and ensembles for iris liveness identification. Recall, F-measure, precision and accuracy are used to evaluate performances of the projected iris liveness identification variants. The experimentation carried out on four standard datasets have shown better iris liveness identification by the fragmental coefficients of cosine transformed iris image with size 4 ∗ 4 using random forest algorithm having 99.18% accuracy immediately followed by an ensemble of classifiers. Copyright © 2023 Inderscience Enterprises Ltd.

Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

Contactless verification is possible with iris biometric identification, which helps prevent infections like COVID-19 from spreading. Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses, replayed the video, and print attacks. The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness. Seven assorted feature creation ways are studied in the presented solutions, and these created features are explored for the training of eight distinct machine learning classifiers and ensembles. The predicted iris liveness identification variants are evaluated using recall, F-measure, precision, accuracy, APCER, BPCER, and ACER. Three standard datasets were used in the investigation. The main contribution of our study is achieving a good accuracy of 99.18% with a smaller feature vector. The fragmental coefficients of Haar transformed iris image of size 8 ∗ 8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size (64 features). Random forest gave 99.18% accuracy. Additionally, conduct an extensive experiment on cross datasets for detailed analysis. The results of our experiments show that the iris biometric template is decreased in size to make the proposed framework suitable for algorithmic verification in real-time environments and settings. © 2023 Tech Science Press. All rights reserved.

Covid-19 Contactless Remedies for Students in Educational Institutes

As we all know fingerprint recognition is one of the secure and accurate Biometric Technologies. If think about it in deep even with the Biometric system the virus can be spread during these situations. To overcome this, we need to come up with a secure and contactless way of authentication. So, let's update to some contactless remedies like Iris authentication which are unique for every individual and they don't need to have any physical contact. So, we can use this Iris detection for a secure and contactless authentication system. The main aim of this research is to provide contactless remedies for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. Coming to the outline of the attendance management system, we will first collect the data from the Kaggle repository. Next, we split the data into training and testing, then we will train the data using transfer learning techniques and test the model by using test data. Finally, we integrated the trained model with the flask. If the Iris matches then the attendance of a particular person will be posted. If not matched then we train the model by adding new person's data. For the construction of modern electronic security systems, real-time face recognition is crucial. Face detection, feature extraction, and face recognition are the three procedures involved. After recognizing the face, it will check whether the person's face matches the collected database. If it matches it will show the person's name, the number of books he took, and what those books are for Library transactions and in the same way the locker will be open if the person's data is matched. The proposed methods are secure and unique contactless ways of authentication for every individual. So, we can use these detection and authentication systems for secure and contactless applications. It can be successfully used for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. The Covid-19 infection in society will undoubtedly decline if the proposed argument is implemented. © 2023 The authors and IOS Press.

Eye Detection from Face Images covered in Face-masks using HAAR features

The contagious illness known as COVID-19 made wearing a mask an essential part of daily life. Mask-covered faces cannot be detected by the current eye detection methods. Many biometric identification systems, like iris recognition, depend on accurate eye detection. Thus, in this study, an efficient method using machine learning for detecting eyes of people wearing mask is presented. Haar-cascade classifier is used to implement real-time eye detection from a live stream via webcam. From the live stream, frames are extracted and saved as images. Dataset was prepared by collecting face images of people wearing mask under various background. Haar-cascade classifier which was trained using 2000 positive and 4000 negative images is used to detect the position of eyes. According to the results on dataset, the system could attain an average accuracy of 96.72%. © 2022 IEEE.

Immune Reconstitution Inflammatory Syndrome Presenting as Disseminated Kaposi Sarcoma.

We present a patient who was previously diagnosed with HIV and had multiple violaceous skin lesions at the time of his diagnosis. Following the initiation of antiretroviral therapy (ART), the number of lesions increased significantly and he developed shortness of breath, which prompted hospital admission for further workup. Biopsy of the skin lesions confirmed the diagnosis of Kaposi sarcoma (KS). Bronchoscopy with biopsy revealed KS lesions in his respiratory system. Imaging and biopsy confirmed KS invasion of lymph nodes. Due to widespread KS, he was diagnosed with immune reconstitution inflammatory syndrome (IRIS). Because of the lack of improvement on ART alone, he was started on chemotherapy, which decreased the size of existing skin lesions, stalled the development of new skin lesions, and led to symptom improvement. As a result of this case, we recommend that treatment teams have close follow-ups of patients started on ART and that they remain mindful of the possibility of IRIS. Disseminated KS may warrant a prompt response with chemotherapy to improve outcomes.

Bilateral acute depigmentation of the iris determined in two cases after COVID-19 infection.

Bilateral acute depigmentation of the iris (BADI) is a rare disease characterized by iris atrophy. Although it can be self-limiting, it is sometimes progressive and can lead to glaucoma and severe vision loss. Two female patients were admitted to our clinic because of a change in iris color following coronavirus disease 2019 (COVID-19) infection. After the exclusion of other etiologies in the eye examination, BADI was diagnosed in both cases. Thus, it was shown that COVID-19 may also be involved in the etiology of BADI.

Tuberculosis-Associated Immune Reconstitution Inflammatory Syndrome-An Extempore Game of Misfiring with Defense Arsenals.

The lethal combination involving TB and HIV, known as "syndemic" diseases, synergistically act upon one another to magnify the disease burden. Individuals on anti-retroviral therapy (ART) are at risk of developing TB-associated immune reconstitution inflammatory syndrome (TB-IRIS). The underlying inflammatory complication includes the rapid restoration of immune responses following ART, eventually leading to exaggerated inflammatory responses to MTB antigens. TB-IRIS continues to be a cause of morbidity and mortality among HIV/TB coinfected patients initiating ART, and although a significant quantum of knowledge has been acquired on the pathogenesis of IRIS, the underlying pathomechanisms and identification of a sensitive and specific diagnostic marker still remain a grey area of investigation. Here, we reviewed the latest research developments into IRIS immunopathogenesis, and outlined the modalities to prevent and manage strategies for better clinical and diagnostic outcomes for IRIS.

A Novel Fusion System Based on Iris and Ear Biometrics for E-exams

With the rapid spread of the coronavirus epidemic all over the world, educational and other institutions are heading towards digitization. In the era of digitization, identifying educational e-platform users using ear and iris based multi-modal biometric systems constitutes an urgent and interesting research topic to pre-serve enterprise security, particularly with wearing a face mask as a precaution against the new coronavirus epidemic. This study proposes a multimodal system based on ear and iris biometrics at the feature fusion level to identify students in electronic examinations (E-exams) during the COVID-19 pandemic. The proposed system comprises four steps. The first step is image preprocessing, which includes enhancing, segmenting, and extracting the regions of interest. The second step is feature extraction, where the Haralick texture and shape methods are used to extract the features of ear images, whereas Tamura texture and color histogram methods are used to extract the features of iris images. The third step is feature fusion, where the extracted features of the ear and iris images are combined into one sequential fused vector. The fourth step is the matching, which is executed using the City Block Distance (CTB) for student identification. The findings of the study indicate that the system's recognition accuracy is 97%, with a 2% False Acceptance Rate (FAR), a 4% False Rejection Rate (FRR), a 94% Correct Recognition Rate (CRR), and a 96% Genuine Acceptance Rate (GAR). In addition, the proposed recognition system achieved higher accuracy than other related systems. © 2023, Tech Science Press. All rights reserved.

Contactless Mask Monitoring and Biometric Attendance System

This paper attempts to give an overview of the system which is designed keeping social distancing guidelines in mind. Our system will detect in real-time, if the person in the captured live video is wearing a mask properly or not using a mask detecting algorithm developed using deep learning and neural networks with an accuracy of 96.05%. If and only if the person is wearing a mask, they will be allowed to scan the iris and hence record their attendance, which can be stored in excel or CSV formats. The location of iris biometric is translated to a real-life position in the 3D space with the resolution of 0.lmm. To scan the located biometric this system comprises a robotic arm. End effector of this robotic arm traverses to the translated position of the person's eye to scan iris with an iris scanner. The system employs a 'four degrees of motion' robotic arm that can autonomously align itself to the iris with an accuracy of 96.86%. It is battery operated and has a cylindrical workspace with maximum range of 300mm, hence it is easily deployable in institutions requiring secure authorization while monitoring COVID-19 safety norms. © 2022 IEEE.

Efficient, accurate and fast pupil segmentation for pupillary boundary in iris recognition

Iris recognition is a robust biometric system-user-friendly, accurate, fast, and reliable. This biometric system captures information in a contactless manner, making it suitable for use during the COVID-19 pandemic. Despite its advantages such as high security and high accuracy, iris recognition still suffers from pupil deformation, motion blur, eyelids blocking, reflection occlusion and eyelashes obscure. If the pupillary boundary is not accurately segmented, iris recognition may suffer tremendously. Moreover, reflections in iris image may lead to an incorrect pupillary boundary segmentation. The segmentation accuracy can also be affected and reduced because of the presence of an unwanted noise created by the motion blur effect in iris image. Additionally, the pupillary boundary might change from circular shape to uneven or irregular shape because of the interference and obstruction in pupil region. Therefore, this work is carried out to determine an accurate, efficient and fast algorithm for the segmentation of pupillary boundary. First, the iris image is pre-processed with Wiener filter. Next, the respective iris image is assigned with a specific threshold. After that, the pixel property in iris image is computed to determine the pupillary boundary coordinates which are acquired from the measured pixel list and area in iris image. Finally, morphological closing is used to remove reflections in the inner region of pupil boundary. All experiments are implemented with CASIA v4 database and Matlab R2020a.

A stitch in time saves my (ne) (vision).

Background: Corneal melt with iris prolapse is a rare complication of autoimmune diseases, especially rheumatoid arthritis. Purpose: To highlight a challenging case of a peripheral ulcerative keratitis (PUK) with corneal melt and iris prolapse in a patient's only eye. Synopsis: A 56-year-old Asian Indian male presented with blurring of vision in the right eye and was diagnosed with cataract. He was a known type 2 diabetes mellitus and a rheumatoid arthritis patient and was not on treatment. He had been previously diagnosed with PUK in the left eye and was lost to follow-up due to coronavirus disease 2019 (COVID-19) after therapeutic penetrating keratoplasty and lost his vision in that eye. Cataract surgery in the right eye was done under cover of immunosuppression. Subsequently, he developed PUK and was treated with a glue and bandage contact lens. Again, he was lost to follow-up and then presented a few months later with corneal melt with iris prolapse in the right eye. We describe in the video the surgical and medical challenges and successful salvage of both the eyeball and the vision. Highlights: Highlights include the following: 1. A rare case of corneal melt with iris prolapse. 2. Demonstration of surgical technique of patch graft. 3. Anterior segment optical coherence tomography before and after the procedure. Video link: https://youtu.be/HbgixlEAYKU.

SARS-CoV-2 virus-induced bilateral acute iris transillumination syndrome: The first case series in literature.

Inflammatory events with highly variable clinical features have been reported with the coronavirus disease 2019 (COVID-19) pandemic, which has affected the whole world. Only one case of COVID-19-associated bilateral acute iris transillumination (BAIT) syndrome has been reported in the literature. We present a case series in which we hypothesize that the immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may have a mechanism similar to the common viruses that cause BAIT, the etiology of which is still unknown.

Machine learning-based iris liveness identification using fragmental energy of cosine transformed iris images

Iris biometric identification provides a contactless authentication preventing the spread of COVID-19 like diseases. These systems are made vulnerable and unsafe because of the spoofing attacks attempted with the help of contact lenses, video replays and print attacks. The paper proposes the iris liveness detection method to mitigate spoofing attacks, taking fragmental coefficients of cosine transformed iris image to be used as features. Seven variants of feature formation are considered in experimental validations of the proposed method, and the features are used to train eight assorted machine learning classifiers and ensembles for iris liveness identification. Recall, F-measure, precision and accuracy are used to evaluate performances of the projected iris liveness identification variants. The experimentation carried out on four standard datasets have shown better iris liveness identification by the fragmental coefficients of cosine transformed iris image with size 4 * 4 using random forest algorithm having 99.18% accuracy immediately followed by an ensemble of classifiers.

Efficient, accurate and fast pupil segmentation for pupillary boundary in iris recognition

Iris recognition is a robust biometric system—user-friendly, accurate, fast, and reliable. This biometric system captures information in a contactless manner, making it suitable for use during the COVID-19 pandemic. Despite its advantages such as high security and high accuracy, iris recognition still suffers from pupil deformation, motion blur, eyelids blocking, reflection occlusion and eyelashes obscure. If the pupillary boundary is not accurately segmented, iris recognition may suffer tremendously. Moreover, reflections in iris image may lead to an incorrect pupillary boundary segmentation. The segmentation accuracy can also be affected and reduced because of the presence of an unwanted noise created by the motion blur effect in iris image. Additionally, the pupillary boundary might change from circular shape to uneven or irregular shape because of the interference and obstruction in pupil region. Therefore, this work is carried out to determine an accurate, efficient and fast algorithm for the segmentation of pupillary boundary. First, the iris image is pre-processed with Wiener filter. Next, the respective iris image is assigned with a specific threshold. After that, the pixel property in iris image is computed to determine the pupillary boundary coordinates which are acquired from the measured pixel list and area in iris image. Finally, morphological closing is used to remove reflections in the inner region of pupil boundary. All experiments are implemented with CASIA v4 database and Matlab R2020a.

Bilateral Acute Depigmentation of Iris (BADI) and Bilateral Acute Iris Transillumination (BAIT)-An Update.

Bilateral acute depigmentation of the iris (BADI) and bilateral acute iris transillumination (BAIT) are relatively new clinical entities characterized by acute pigment dispersion from the iris stroma or iris pigment epithelium, respectively. While BADI presents with diffuse or geographic areas of iris stromal depigmentation without transillumination, BAIT cases typically develop diffuse iris transillumination and mydriatic atonic pupils. Prolonged pigment dispersion and ocular hypertension are more common in BAIT. Although the exact etiopathogenesis is still unknown, moxifloxacin toxicity appears to be a probable/likely cause. The underlying cause of BADI or BAIT in patients who were not exposed to fluoroquinolone antibiotics remains unexplained. Systemic viral infections, including coronavirus disease 2019, may be the triggering event in several cases.