AI-biometric driven Smartphone App for strict Post-COVID Home Quarantine Management

This repository contains the code implementation for our paper.

• License
• Abstract
• Demo
• Press Release
• Contributors

COVID-19 has been announced as a Global Communal Health Extremity by WHO on January 2020. Meaningful preventive solutions have been taken with smartphone selfie/geofencing apps toward managing mandatory home quarantine and physical distancing. In the post-COVID world, fast screening and strict quarantine can play a crucial role in bringing back normality. Quarantine being offered at home can be a comfortable solution for both government and patients. On the other hand, it can be hazardous if not strictly followed and adequately realized. However, the existing geofencing/face selfie apps take static photographs and location data at certain time intervals that can allow patients to violate the rules between those periods, thus failing to ensure active user identity. To realize unbreached home quarantine policies, this article introduces a CUBA-HQM smartphone app that performs continuous user biometric authentication (CUBA) augmented with geofencing using AI technology. The purpose of continuous tracking is to strictly control the spread of infectious diseases in society by monitoring the individual move in/out in the quarantine zone.

• IIT Mandi
• The Hindu
• The Economic Times
• India Today

• Rohit K Bharadwaj : Developed the Android Application, integrated geo-fencing and utilized latest face recognition models into the app.
• Piyush Goyal : Developed the Backend Server and integrated facial recognition system in the backend.
• Gaurav Jaswal : Supervision, Paper Writing, Figures.
• Daksh Thapar : Supervision, Paper Writing, Figures, Backend coding.
• Aditya Nigam : Initial Idea, Supervision, Guidance, Problem Statement Formulation.
• Kamlesh Tiwari : Supervision, Guidance.

• MIT license