The Facial Recognition Technology in Academic Attendance: A Comparative Study for Real-Time Management

In today’s academic settings, managing daily tasks like attendance tracking has become increasingly burdensome. Traditional methods of manual attendance taking are inefficient and time-consuming, particularly with growing numbers of students and staff. To address this challenge, various approaches, including face identification systems, have been developed. This research introduces a smart face recognition (FR) system for managing attendance efficiently. The system employs multiple face recognition methodologies such as Local Binary Histogram, PCA/Eigen Face Recognizer, and Fisher Face Recognizer, enhancing system performance. These methods are combined using Ensemble Fusion to improve accuracy. Additionally, the system utilizes Multitask Cascaded Convolutional Network for face detection and attribute extraction. Extracted attributes are matched with stored facial templates to identify recognized faces and mark attendance. Integration with Cloud API facilitates record-keeping. The system also includes a feedback and notification system for process status indication. Results indicate that the proposed system achieves 82.1% accuracy in face recognition and requires minimal time (0.000081s) to predict and mark attendance.