Smart Attendance Marking System using Face Recognition

Abstract

This thesis uses facial recognition technology to automate attendance-taking in educational settings, which can save time. Facial recognition has many applications and is a hot topic in recent years. This thesis aims to create a unique approach to student recognition and performance prediction by using image recognition and machine learning techniques. By comparing student faces to a pre-existing database, this thesis employs facial recognition technology to track attendance and forecast academic success in a classroom setting. The study presents approach to student recognition and academic performance prediction using image recognition and machine learning techniques. The study shows that the proposed method is effective and feasible despite potential concerns. The investigation also explores limitations and suggests future research to improve accuracy and refine the user interface. The study's conclusion raises the prospect of using advances in face recognition and artificial intelligence to address important issues in education, including identifying students at risk of underperformance and delivering prompt interventions without human intervention.