Abstract
Considering that most of online training is not effectively supervised, this article presents an online leaning state assessment approach which combines blink detection, yawn detection, and head pose estimation. Blink detection is realized by computing the eye aspect ratio and the ratio of closed eye frames to the total frames per unit time to evaluate the degree of eye fatigue. Yawn detection is implemented by computing the aspect ratio of the mouth by using the feature points of the inner lip and combining it with the time of opening mouth to distinguish the mouth state. Head pose estimation is first implemented by calculating the head rotation matrix by matching the feature points of 2D face with the 3D face model and then calculating the Euler angle of the head according to the rotation matrix to evaluate the change of the head pose. Especially in yawn detection, we employ the feature points of inner lips in the calculation of the mouth aspect ratio to avoid the impact of lip thickness of various participants. Furthermore, the blink detection, yawn detection, and head pose estimation are first calculated based on the two-dimensional grayscale image of human face, which could reduce the computational complexity and improve the real-time performance of detection. Finally, combining the values of blinking, yawning, and head pose, multiple groups of experiments are carried out to assess the state of different online learners; then, the learning state is evaluated by analyzing the numerical changes of the three characteristics. Experimental results show that our approach could effectively evaluate the state of online learning and provide support for the development of online education.
Highlights
In recent years, as the online education industry evolved rapidly, many forms of education arose, establishing a dualmode ecology of education [1]
Online learning has become extremely important due to the popularity of the Internet, especially under the effect of the novel coronavirus pneumonia (NCP) [2], and online education has ushered in an unprecedented explosive growth
To solve the above problem, we propose a method of learning state assessment in online education based on multiple facial features detection, which mainly includes blink detection, yawn detection, and head pose estimation
Summary
As the online education industry evolved rapidly, many forms of education arose, establishing a dualmode ecology of education [1]. Sun et al [11] designed a model of emotion calculation based on face characteristics as input data in order to assess the emotional status of learners in the Computational Intelligence and Neuroscience current remote education system and implemented face emotion detection in the real time with SVM algorithm. Kong and Li [17] employed the AdaBoost face detection algorithm to detect the face region and extracted the features of the eye and mouth of online learners according to the facial expression model; they used comprehensive strategies to evaluate the learner’s condition and identified the learning state as focus, tiredness, and normal. To solve the above problem, we propose a method of learning state assessment in online education based on multiple facial features detection, which mainly includes blink detection, yawn detection, and head pose estimation. Multiple groups of experiments are carried out to assess the state of different online learners, and results show that our proposed method could effectively evaluate the state of online learning and provide support and help for the development of online education
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