Facial Recognition Attendance System
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Abstract
This research aims to develop a registration system for event participation using face detection to increase efficiency in time management, reduce errors in data recording, and effectively verify the identity of event participants. We use a facial recognition model developed with deep learning techniques such as Convolutional Neural Networks (CNN) combined with You Only Look Once (YOLO) face detection technology. The application was developed with Python and Node.js based on KOA.js web framework. User database and activity history are stored in MySQL. We conducted 1000 tests under the following conditions: the system was tested under indoor lighting. The distance between camera and the target face(s) are 0.5, 1, and 2 meters, with number of target faces being 1, 2, and 4. Our work compared the detection accuracy between 4 and 8 prototype faces per person. The results found that 1) The higher number of prototype images significantly improves the accuracy and efficiency of face detection. 2) The shortest distance between the camera and the face target gives the most accurate results. 3) Fewer target faces in each face detection session result in higher accuracy. 4) The face detection activity registration system shows an average face detection performance of 0.17 seconds per detection session. This can help reduce registration time. 5) The system efficiently confirmed the identity of event participants, with an average accuracy of 70 percent.
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