(untitled) (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The drastic development of computing technologies has abetted the implementation of Automation in Education (AE) applications. The Automation in Education domain refers to the implementation of a machine supported technologies like Machine Learning (ML), Deep Learning (DL), Convolution Neural Networks (CNN), and Artificial Intelligence (AI) to facilitate the process of teaching the students, taking and maintenance of online attendance, capturing the activities of the students in the classroom, automatic face detection, and recognition, identification of prohibited objects in the classrooms and the most challenging automatic online proctoring of online assessments. In the field of education, attendance management of students is a very crucial task to handle. Many researchers try to attempt this problem. In the current era of machine learning, they have tried to automate this time-demanding task. In the case of the human, each face has unique features. The face recognition algorithm uses this concept and provides the solution. This paper provides a survey of the different ML techniques, which explains the face recognition task, through which one can automate this time-consuming attendance process. We also provide a brief explanation of the recent techniques and their performance comparison.