Next-generation attendance tracking: Automated face detection for lecture attendance

Abstract

Face detection technology has garnered much interest due to its potential uses in industries like face identification and video surveillance systems. In addition to being a component of the automatic face recognition framework, real-time face detection is now becoming its study area. There are numerous methods to handle the problem of face detection. Traditional methods of manual attendance tracking in lecture halls are time-consuming, prone to errors, and lack efficiency. Next-generation attendance tracking systems are incorporating automated face detection technology to address these challenges. This paper explores the implementation of automated face detection for lecture attendance, leveraging computer vision and facial recognition algorithms. Initially, students’ data were gathered and preprocessed using a Median filter (MF). Then the preprocessed data features were extracted using a histogram of gradients (HOG). And finally, extracted features were classified using a Backpropagation Bayesian neural network (BBNN). To prove the efficiency of the suggested method, that is compared and contrasted with specific conventional methods. Experimental outcomes show that the recommended method performs superior when contrasted to traditional methods.