Automated Student Attendance Recognition Using Image Segmentation and Artificial Intelligence

Abstract

Classroom attendance plays a vital role in academic management, yet traditional manual methods are time-consuming, error-prone, and susceptible to proxy attendance. This study proposes an automated attendance system that leverages image segmentation and artificial intelligence to enhance accuracy and efficiency. The system employs image segmentation techniques to isolate and detect individual student faces from classroom images, followed by feature extraction and recognition using AI-based models. By integrating these methods, the system automatically marks attendance in real time, reducing the need for manual intervention. Experimental evaluation demonstrates that the approach not only minimizes false recognition but also provides a scalable and non-intrusive solution for classroom environments. The proposed framework contributes toward smart education systems by ensuring reliable, fast, and secure attendance monitoring.