How Two Indian States Kick-started AI-based Attendance Systems in their schools

RailTel Corporation of India Ltd has recently finished implementing Artificial Intelligence (AI) based Identification System for Capturing Attendance and Management of SDMIS (Student Database Management Information System) for government schools in Assam.

Within four months, RailTel has configured, customized, and deployed this AI-based Identification System for collecting attendance in 48,000 schools throughout Assam’s 33 districts, including elementary, secondary, and upper secondary institutions.

RailTel’s overall project cost is INR 19.20 crore, with INR 12 crore being a one-time expenditure that was released when the project was completed. Out of the remaining budget, INR 3.96 crore is the project’s annual maintenance cost, with a two-year Annual Maintenance Contract (AMC) under the present scope of work. This is one of Axom Sarba Shiksha Abhijan Mission’s initiatives to implement user-friendly digital reforms and information and communication technology (ICT) technologies. RailTel is also offering end-user training for the project’s seamless execution, in addition to providing the AI-based solution to schools in Assam.

Having a good attendance record is a must for students. In some schools failing to have a minimum attendance in an academic year can result in disqualifying a student from appearing for examinations. However, taking attendance is a cumbersome and time-consuming activity both for teachers and students. Further, manual attendance is inherently prone to proxies and manual errors. 

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This is not the first state to have deployed an AI-based attendance system in schools. Last year, the Tamil Nadu e-Governance Agency (TNeGA) had completed a pilot test of an AI-based facial recognition system, with intentions to install it in 3000 schools across the state. This system employs an Image Analytics Device (IAD), a low-powered Edge Computing device that collects video feeds from an embedded camera/webcam/IP camera linked over the network, in addition to artificial intelligence and computer vision. It recognizes faces in the recorded photos, classifies users, and logs attendance in the database. 

Overall, the system went through five phases before being deployed for marking attendance:

1. Data collection phase — a short video of each student is taken from various angles, with an emphasis on facial points.
2. Pre-processing phase — each video clip is cropped and cleaned in order to extract the face thumbnail of each student, with only 50 such pictures available for each student.
3. Training phase — The deep neural network-based facial recognition engine is trained using cropped and condensed picture data from students.
4. Evaluation phase — The trained ML model is next put to the test using a collection of photos it has never seen before. Continuous training has resulted in an accuracy of 99.6 percent.
5. Deployment phase — After then, the model is installed on the IAD, which recognizes the students and records their attendance.

Unlike previous AI-based attendance systems, this facial recognition model can identify and distinguish between identical twins and similar-looking siblings. It can also recognize students with spectacles, students wearing shawls/burkha, students under varied illumination, even when they aren’t making eye contact with the camera.

This facial recognition attendance system also eliminates the need of using sensor-based fingerprint biometrics attendance and enforces the possibility of mass attendance in a short time. Therefore, it is an upgrade from both manual and biometrics-based attendance systems.