AI-Based Smart Classroom Attendance System with Engagement Analytics
Year: 2026
Type: article
Abstract: Conventional classroom attendance systems are ineffective, prone to errors, and fail to provide insights into student engagement or behavioral dynamics. This paper talks about AI-Based Smart Classroom Attendance System with Engagement Analytics (ASCAS), a full-stack, ready for production app that uses deep learning to recognize faces, analyze emotions in real time, find phones, and create role-based multi-user dashboards to automate attendance marking while also measuring how engaged students and teachers are. The system uses a hybrid face recognition pipeline that combines InsightFace (ArcFace model) for 512-dimensional deep embedding extraction and Local Binary Pattern Histograms (LBPH) for texture-based fallback verification. It can recognize faces 80% accuracy on standard laptop hardware (Intel R620 integrated GPU) and 90–95% accuracy on dedicated GPU workstations. There are three types of users that can use it: There are three types of people: students, teachers, and guests. Students and teachers sign up through a webcam-based registration portal that records facial embeddings in a MySQL relational database.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W7154584508
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