Published Paper Details:

The increasing need for efficient and automated attendance management systems has led to the development of face recognition-based solutions. Traditional methods such as manual attendance and RFID systems are prone to inaccuracies and inefficiencies. This research presents an automatic attendance system using Raspberry Pi and the Viola-Jones algorithm, integrated with OpenCV for real-time face detection and recognition. The Viola-Jones algorithm, known for its rapid and accurate face detection, is employed to identify faces in a live video feed captured by a camera module connected to the Raspberry Pi. The system preprocesses the images, extracts facial features, and matches them against a pre-stored database for authentication. Experimental results demonstrate that the proposed system achieves high accuracy and efficiency in different lighting conditions and facial orientations. The integration of Raspberry Pi ensures a low-cost, portable, and scalable solution suitable for academic institutions and workplaces. Conclusively, the study highlights the effectiveness of the Viola-Jones algorithm in real-time attendance monitoring, providing a secure, contactless, and reliable method for identity verification. Future enhancements could include deep learning techniques to improve recognition accuracy and adaptability in complex environments. This research contributes to the growing demand for automated biometric attendance systems by offering a practical and cost-effective solution.