Key points are not available for this paper at this time.
The use of m-learning or other remote education continue to increase due to its ability to reach people who don't have access to campus. Exams are important components of educational programs as well as on an online learning program. In an exam, a proctoring method to detect and reduce the cheating possibility is very important to ensure that the students have learned the material given. Various methods had been proposed to provide an efficient, comfortable online exam proctoring. Start with implementing an exam design with hard constraints in a no proctoring exam, a remote proctoring using a webcam, a machine based proctoring and finally research on automated online proctoring. A visual verification for the whole exam session is needed in an online exam, therefore a face verification is needed. A remaining problem in face recognition area is the system robustness for pose and lighting variations. In this paper, we proposed a method to enhance the robustness for pose and lighting variations by doing an incremental training process using the training data set obtained from m-learning online lecture sessions. As a result, the design of the proposed method is presented in this paper.
Asep et al. (Mon,) studied this question.