FaceTrack: An Intelligent Face Recognition-Based Attendance System

Attendance management is a crucial aspect of academic institutions and organizational administration, as it directly affects discipline, performance evaluation, and record maintenance. Traditional methods such as manual registers and cardbased systems are time-consuming, error-prone, and vulnerable to proxy attendance and data manipulation, highlighting the need for a more reliable solution. This paper presents an intelligent attendance system based on face recognition technology that utilizes computer vision and machine learning techniques for real-time face detection and identification. The system captures images through a camera, detects faces using advanced algorithms, and recognizes individuals by comparing facial features with a pre-trained database using methods such as Haar Cascade, LBPH, or deep learning models like CNNs. It automatically marks attendance without physical interaction, reducing human effort and eliminating fraudulent practices while maintaining accurate digital records for easy storage and analysis. Furthermore, the system enhances security, transparency, and operational efficiency. Experimental results indicate that the proposed system achieves high accuracy and performs efficiently under varying conditions, making it suitable for real-time applications in classrooms, offices, and other environments. Index Terms: Face Recognition, Attendance System, Computer Vision, LBPH, Automation