Automated Attendance System for Rural Schools

Shubham Sanjay Yadav , Pranav Bhor , Sarthak Rajput , Prachi Bhure

face recognition, school attendance, rural India, webcam, HTML5 Canvas, hybrid similarity, React, Express, PostgreSQL, TypeScript, BharatNet, Drizzle ORM, Zod validation, cosine similarity, Euclidean distance, 128-number face descriptor, browser-based encoding, 96.5% accuracy, 10 Mbps deployment, Digital Personal Data Protection Act 2023, Ajeenkya DY Patil University, proxy attendance, manual fallback, dashboard charts, rural schools

In rural schools in India, the process of tracking the attendance of students is still mostly manual, with teachers calling out students or handing out paper registers. It is a slow, unreliable and manipulated method of assessment- a student can have a friend mark him/her present even when he/she is not even in school. In the long run, the wrong records cause issues when examining students and also complicate the work of the administrators to identify students who are skipping classes. The following paper outlines an attendance management system which tracks the attendance of students automatically with the help of a webcam and a face recognition which does not require any costly hardware or an internet connection that is fast enough to support cloud-based AI processing. The recognition engine is completely implemented within a web-based application based on the HTML5 Canvas API - 25 quick webcam snapshots are taken during registration, a compact 128-number representation of the face of the student is created, and any new snapshots are compared with existing representations by a scoring formula based on two complementary distance measures. The testing on sixty students in three lighting conditions revealed that the system was accurate in identifying the student's 96.5 percent of the time in normal classroom lighting and 84.7 percent of the time during harsh window light illuminating students, and that each recognition check took approximately 321 milliseconds. The entire site, web interface, server, and reporting tools, has been developed in TypeScript, React, Express, and PostgreSQL and can be accessed on a cloud server at 10 Mbps, which is the maximum bandwidth most rural schools in India have under the BharatNet programme. Teachers are also able to manually mark attendance where necessary, download records in spreadsheet files as well as simple charts that indicate classes with low attendance. The paper captures the technical details, test results and deployment in four tables. Keywords: face recognition, school attendance, rural India, webcam, HTML5 Canvas, hybrid similarity, React, Express, PostgreSQL, TypeScript, BharatNet

Volume 14 Issue 4, April-2026

Pages : d943-d953