Smart and Contactless Person Identification System in the COVID 19 Pandemic Process

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%.