International Journal for Advanced Research in Science & Technology

This study presents a novel automatic class attendance system utilizing biometric facial recognition based on a Raspberry Pi and a camera module.The detection algorithm operates by analyzing several facial features, including the distance between the eyes, forehead to chin distance, nose to mouth distance, eye socket depth, cheekbone shape, and lip, ear, and chin contours.The system follows a four-step process: capturing and scanning, extracting features, comparing, and matching the captured images.Designed for real-time recognition, this system automates the attendance tracking process, replacing traditional manual methods.Leveraging the Raspberry Pi 3 Model B+ microprocessor and open-source Python libraries, particularly OpenCV, the facial recognition algorithm efficiently records attendance.The system demonstrates exceptional performance, achieving a 100% accuracy rate with a detection time of just 1 second.It is capable of capturing a student's face from a distance of 1.25 cm and can recognize faces at orientations of 0 and 45.With its rapid and reliable performance, this facial recognition system is highly suitable for integration into various applications.