ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
At present, COVID-19 is still spreading and affecting millions of people worldwide. Minimizing the need for travel can significantly reduce the probability of infection and improve patients’quality of life. The wireless body area network (WBAN) transmits the patients’physiological data to the doctor remotely through the sensors in a way that minimizes physical contact with others. However, existing WBAN security authentication schemes have core limitation that includes weak authentication performance and over-consumption of resources that precludes their widespread adoption in practical applications. Therefore, in this paper, an enhanced dual-factor authentication system that address the mentioned drawbacks is proposed for securing WBAN resources. By combining iris and electrocardiogram (ECG) features, users would be required to pass the first-level iris authentication before performing the second-level ECG authentication, thus enhancing the overall security scheme of a WBAN system. Furthermore, we examined the existing Inter-Pulse-Intervals (IPI) encoding methods and propose a more efficient ECG IPI encoding algorithm which can effectively shorten the encoding time without affecting the overall encoding performance. Finally, extensive experiments were performed to verify the performance of the proposed dual-factor iris and ECG based WBAN authentication system using public iris and ECG databases. The experimental results show that the false acceptance rate (FAR) is close to 0.0% and the false rejection rate (FRR) is close to 3.2%. Findings from this study suggest that the proposed dual-factor authentication scheme could aid adequate deployment of security schemes to protect WBAN resources in practical applications. IEEE
ABSTRACT
Selfie-based biometrics has great potential for a wide range of applications since, e.g. periocular verification is contactless and is safe to use in pandemics such as COVID-19, when a major portion of a face is covered by a facial mask. Despite its advantages, selfie-based biometrics presents challenges since there is limited control over data acquisition at different distances. Therefore, Super-Resolution (SR) has to be used to increase the quality of the eye images and to keep or improve the recognition performance. We propose an Efficient Single Image Super-Resolution algorithm, which takes into account a trade-off between the efficiency and the size of its filters. To that end, the method implements a loss function based on the Sharpness metric used to evaluate iris images quality. Our method drastically reduces the number of parameters compared to the state-of-the-art: from 2,170,142 to 28,654. Our best results on remote verification systems with no redimensioning reached an EER of 8.89% for FaceNet, 12.14% for VGGFace, and 12.81% for ArcFace. Then, embedding vectors were extracted from SR images, the FaceNet-based system yielded an EER of 8.92% for a resizing of x2, 8.85% for x3, and 9.32% for x4.
ABSTRACT
Biometric authentication has grown significantly since the events in the United States in 2001. Moreover, during the COVID-19 pandemic, many more researches have been done on contactless biometric authentication or recognition methods. Arduino Mega 2560 is a module often used in designing applications, but most of the time it can only be used as an interface in image processing. During the post-doctoral researches in multimodal biometrics, it was developed a complex application using the Arduino Mega 2560 board and several sensors attached to it, including two fingerprint sensors, an infrared imaging camera, a keyboard and a Bluetooth module that allows communication between this device and a mobile device or a desktop / laptop system. The developed application can be used for registration or authentication to an internet banking system, by entering the biometric features represented by iris and fingerprint, in addition to the classic authentication methods, based on username and a password generated by a digipass. The application and the device only have the role of taking the iris or the fingerprint, these being transmitted via Bluetooth for processing on a mobile or laptop device. Biometric analysis and extraction cannot be performed on the Arduino Mega 2560 due to the low frequency of the processor (12 MHz only) and the relatively small memory space.
ABSTRACT
COVID-19 disease, caused by the SARS-CoV-2 virus, has led to many changes in the movement of people in different environments or even between different countries. Vaccines began to be administered more than a year after the pandemic has started. Following the administration of the vaccine, various ways were sought to identify the vaccinated people very quickly, in order to allow access to various areas, such as supermarkets or secure areas at airports. Thus, digital certificates were issued for attesting the vaccination, testing or recovery of that person. These certificates contain a QR code that can be scanned using an application installed on a mobile device. Research has sought to identify a more secure way to identify the holders of such a certificate. After vaccination, we consider it's useful to insert the biometric data of the iris or fingerprint in a national or international database, from where it can be accessed by all institutions authorized to verify the validity of such a certificate. During the research, the human iris was taken as a biometric feature, trying to find ways to scan it in real time and without a great interaction of the user with the video capture device. One of the biggest problems with such an approach is the exact connection between the person whose iris was scanned and the proof of having a COVID digital certificate. The idea was to replace the need to hold a certificate in printed or digital form with the image of the human iris, which in polar coordinates is quite similar to that of a QR code.
ABSTRACT
In recent years, the topic of contactless biometric identification has gained considerable traction due to the COVID-19 pandemic. One of the most well-known identification technologies is iris recognition. Determining the classification threshold for large datasets of iris images remains challenging. To solve this issue, it is essential to extract more discriminatory features from iris images. Choosing the appropriate loss function to enhance discrimination power is one of the most significant factors in deep learning networks. This paper proposes a novel iris identification framework that integrates the light-weight MobileNet architecture with customized ArcFace and Triplet loss functions. By combining two loss functions, it is possible to improve the compactness within a class and the discrepancies between classes. To reduce the amount of preprocessing, the normalization step is omitted and segmented iris images are used directly. In contrast to the original SoftMax loss, the EER for the combined loss from ArcFace and Triplet is decreased from 1.11% to 0.45%, and the TPR is increased from 99.77% to 100%. In CASIA-Iris-Thousand, EER decreased from 4.8% to 1.87%, while TPR improved from 97.42% to 99.66%. Experiments have demonstrated that the proposed approach with customized loss using ArcFace and Triplet can significantly improve state-of-the-art and achieve outstanding results.
ABSTRACT
The COVID-19 pandemic has highlighted the limitations of the most popular biometric authentication systems. It is thus necessary to develop other biometric technologies to ensure that they there are alternatives when some solutions are no longer viable. This paper presented a stand-alone biometric authentication system for computers used in industrial applications. The accuracy metric used to assess the system was the equal error rate (EER) and this was used to calculate an overall system accuracy of 88.94%. The stand-alone device had limited processing power and this resulted in an overall authentication time of 3.966s. In general the system had an acceptable accuracy and some security measures were put into place to bolster the overall system security.
ABSTRACT
There has been a steep rise of contactless payment during COVID-19. The rapid improvements of miniaturized sensors and biometric recognition systems for face identification, fingerprint, iris, and voice are conducive and fit during this rise of COVID-19. Thus, non-contact interactions are the most effective way to fight against the spread of the virus and any other diseases. One of the most used is iris scanners and speech recognition. The study promotes contactless payments to address the accompanying issues in cash aid distribution particularly in the DSWD 4Ps, where it has a two-Tier biometric security system which is iris recognition and speech recognition. This can provide the same type of service and securities as a normal ATM while removing the worry of getting different kinds of viruses and diseases. Testing the iris recognition system, a False acceptance ratio of 13% and 3% of False Rejection rates were achieved. While for the testing of speech recognition (security questions), a False Acceptance Ratio of 0% and False Rejection Ratio of 12.12% were achieved. Lastly, testing of speech recognition (navigation)a False Acceptance Ratio of 0% and False Rejection Ratio of 3.62% were achieved. Giving the system an 84% accuracy for the iris recognition, 87.88% for the security questions, and 96.36% for the navigation. © 2021 IEEE.
ABSTRACT
The Covid 19 Pandemic has had an impact on many aspects of our daily lives such as Restricting contact through touch, wearing masks, practicing social distancing, staying indoors which has led to change in our behaviors and prioritized the importance of safety hygiene. We travel to different places such as Schools, Colleges, Restaurants, offices, and Hospitals. How do we adapt to these changes and refrain from getting the virus? Luckily, we have the technology to aid us. We are all used to biometric systems for marking our Presence/ Attendance in places like colleges, Offices, and Schools with fingerprint sensors, fingerprint sensors use our Fingerprint to mark our presence however Covid 19 has restricted the use of touch causing problems in marking attendance. One way to resolve the problem is using Artificial Intelligence by using a Recognizer to identify people with their face and iris features. We implement the Face Recognition and the Iris Recognition using two models which run concurrently, one to Recognize the Face by extracting the features of the face and passing the 128-d points to the Neural Network (Mobile net and Resnet Architecture). which gives the identity of the person whose image was matched with the trained database and the other by extracting iris features to recognize people. For extracting iris features we use the Gabor filter to extract features from the eyes which are then matched in the database for recognition using 3 distance-based matching algorithms city block distance, Euclidean distance, and cosine distance which gives an accuracy of 88.19%, 84.95%, and 85.42% respectively. The face Recognizer model yields an Accuracy of 98%, while Iris Recognizer yields an accuracy of 88%. When these models run concurrently it yields an accuracy of 92.4%. © 2021 IEEE.