Precise Presence Calculating System Using HAAR Cascade and CAFFE Model

Abstract

Attendance management is often time-consuming and prone to proxy attendance. Traditional methods like fingerprint and RFID systems lack reliability and efficiency. This system addresses these limitations with technologies such as Haar cascade for face detection, OpenCV-Python for image processing, a Caffe model for deep learning-based face detection, and Support Vector Machines (SVM) for facial recognition, with Pandas ensuring real-time data processing. Utilizing facial recognition technology, the system streamlines attendance recording, making it efficient, accurate, and user-friendly while reducing manual workload. The implementation involves four phases: Image Capture, Group Image Segmentation and Face Detection, Face Comparison and Recognition, and Attendance Database Updating. By automating these processes, the system enhances accuracy and reliability, providing an effective solution for classrooms and workplaces to manage attendance efficiently.