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.
ABSTRACT
The coronavirus has affected millions around the world and has inevitably brought about a necessity to wear face masks in official and public places to take the first step in keeping one’s self safe. To monitor personnel and public areas and prevent the spread of the disease we present a scalable and deployable face mask detection system in a real time setting using a novel hide and seek algorithm. Our model, based on openCV library and dlib environment utilizes the facial landmarks where in the algorithm detects face masks through the presence and absence of facial markers. We call this process as seeking and hiding. We overcome present issues of high computational cost of deep learning models and low inference speeds of general detection paradigms. We also validate our algorithm on several aspects which affect the accuracy of other models such as image and face orientation, type of face masks and more. As our model requires no data for model training, we eliminate the highly sensitive issue of acquiring facial data and bias. Our model achieves 98.79 % precision and 94.81 % recall. © 2021, Springer Nature Switzerland AG.