Student Class Attendance and Interest Assessment System with Facial Expression Detection via Webcam
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Abstract
This research aims to implement a student class attendance and interest assessment system with facial expression detection using a webcam, operating in a real-time environment within the classroom. The expressions on the faces of the students based on emotion dataset including "Neutral", "Happy", "Surprised", "Angry", "Sad" and "Fear" can be examined as attention and inattention. This system can be divided into two parts: the teacher section and student section. On the teacher section, there will be a dashboard displaying all students' details logging in to the course of study and the percentage of individual student attention in each subject. On the other hand, the student section will be able to log in by joining the class via the teacher's given code. Also, students can check their interest status. MobileFaceNet model was selected as a face detector. In the meantime, the mini-Xception model was also selected as facial expression detection. Therefore, the emotional status of students is to be detected via webcam by using MobileFaceNet and mini-Xception. Facial emotional expression status can be divided into two groups: Facial emotions are "normal, happy, surprised", showing interest. Simultaneously, the facial emotion groups are "angry, sad, afraid", showing their indifference. After finishing the study, the system will calculate the student's interest and disregard percentage and student attention as a summary report. A representative sample group of 4 students tested for finding attention and inattention in the classroom three times for 30 minutes. The overall results show that students are interested in 82.81% and disregard 17.19%.
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References
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