Year: 2025
Type: article
Abstract: The demand for efficient and accurate attendance systems in educational institutions has grown substantially, motivating the development of a contact-free, AI-driven solution. This research presents a Real-Time Student Attendance System that leverages computer vision and facial recognition to automate attendance recording with high accuracy. The system captures student faces using a standard webcam, applies Haar cascade detection and Local Binary Pattern Histogram (LBPH) recognition, and logs attendance automatically into structured CSV files. A user-friendly Tkinter GUI facilitates module navigation—student registration, model training, real-time recognition, and report generation—while supporting manual override when necessary. The system’s modular architecture ensures seamless integration of components and robust performance under varying environmental conditions. Testing demonstrates over 95% recognition accuracy, with immediate generation of attendance summaries and real-time GUI feedback. The proposed system reduces administrative load, prevents proxy marking, and offers scalability as a practical, low-cost solution for modern classrooms.
Author: Vaishnavi Lambu
Institution: Visvesvaraya Technological University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4413600530