MULTIPLE FACE DETECTION SYSTEM USING DEEP LEARNING

Abstract

The smart classroom leverages automation to streamline tasks such as attendance registration, which traditionally require considerable time and effort. Conventional methods—including identification cards, radio frequency systems, and biometric technologies—often face limitations related to safety, accuracy, and cost. However, recent advancements in digital image processing, particularly face recognition technology, present a promising alternative.This study introduces an automated attendance system utilizing the YOLOv8 algorithm, capable of detecting and recognizing multiple student faces simultaneously with high efficiency. The system was tested on a real time dataset and achieved up to 90-95% accuracy, highlighting its reliability and effectiveness in automating attendance processes. The proposed system not only automates the attendance marking process but also generates analytical reports. Face recognition plays a vital role in uniquely identifying individuals, making it an ideal solution for classroom attendance automation through the integration of advanced face detection techniques.