Automated system for identifying vaccinated person by iris recognition technique using deep learning models.

COVID-19 is an infectious disease which is spreading fast and some variants of the coronavirus, such as Delta and Omicron, are spreading more easily between people and this spreading can be alleviated to some extend by vaccination. In this context, the government is demanding for producing the vaccination certificate for the person to be in certain public places. But there can be chances where people do not know their Aadhar number and they do not carry their vaccination certificates especially in rural areas. And also it takes a lot of time in waiting in a long queue for verification purpose. So to tackle all these problems we propose an automated system for identifying vaccinated person without much human interventions. The system uniquely identifies the person through iris recognition using various deep learning models. In this paper we made a comparative analysis of different deep learning and CNN architectures (MobileNet, EfficientNetB3,InceptionV3, DenseNet169) for building a model for person recognition and select the best model with maximum accuracy. Here, we used kaggle dataset (1000 iris images) for testing and training. Also using an iris scanner, the live iris images can be captured and used for model building. Using some deep learning algorithms, the features of iris image of the person is obtained and it is compared with those in Aadhaar database to get the Aadhar number. Using this Aadhar number, the details of the vaccinated person can be obtained from vaccination database and system will identify whether a person is vaccinated or not. By installing our proposed system, vaccinated person can be easily identified without much human interventions, avoiding touch and reducing the long queue for verification, thereby maintaining social distancing. [ABSTRACT FROM AUTHOR]