MULTI-FACE RECOGNITION BASED ATTENDANCE SYSTEM

Abstract

Attendance management is vital for monitoring discipline in educational institutions. Traditional methods like manual roll calls are inefficient and prone to proxy attendance. This project proposes a Real-Time Multi-Face Recognition Based Attendance System using computer vision and AI. The system detects multiple faces simultaneously and matches them against pre-stored data, logging the attendance in an Excel/CSV file. This contactless solution ensures accuracy and provides a secure database for tracking. Conventional attendance tracking has long depended on outdated manual practices, such as paper-based logs or proximity card scanning. These traditional approaches are not only labor-intensive but also highly susceptible to inaccuracies and proxy attendance. To address these inefficiencies, the Multiple Face Detection Attendance System utilizes advanced artificial intelligence and computer vision to automate the identification process. The core of this technology rests on sophisticated mathematical algorithms and pattern recognition that ensure precise biometric verification. When the system identifies a face and validates it against a pre-registered template, it instantly updates the digital attendance record. This data is housed in a centralized, secure database, providing administrators with a streamlined way to generate comprehensive reports, monitor long-term attendance patterns, and integrate data directly into payroll systems.