Real-Time Image Processing: Face Recognition based Automated Attendance System in-built with “Two-Tier Authentication” Method

With the advent of computer vision, there has been significant growth in the research and development of facial recognition based automated attendance systems. Although current systems have been successful in alleviating human interaction and manual efforts, there still exist several challenges such as severe misclassifications, undetectable face angles, and different lighting conditions which result in a drastic drop in the accuracy. The system introduced in this paper has achieved an overall accuracy of 93.33%. A concept termed the “two-tier authentication” method has been developed to improve the overall accuracy of the system and to integrate a mechanism of time allowance for students. This method facilitates granting attendance to students based on the number of recognized faces as well as the probability of each prediction allowing for a more robust method of marking attendance to students. The novelty of this approach is to introduce an accurate statistical sequence for the execution of a proxy-free automated attendance system that employs state-of-the-art algorithms. Composed of 3 distinct parts, every sub-system performs a specific task namely, face detection, generation of face embeddings (FaceNet), and face classification. A comparative study was carried out to select the most appropriate detection and classification algorithms where Faster R-CNN and Support Vector Classifier outperformed their corresponding competitors respectively.