Abstract
Confusion detection systems (CDSs) that need Noninvasive, mobile, and cost-effective methods use facial expressions as a technique to detect confusion. In previous works, the technology that the system used represents a major gap between this proposed CDS and other systems. This CDS depends on the Facial Action Coding System (FACS) that is used to extract facial features. The FACS shows the motion of the facial muscles represented by Action Units (AUs); the movement is represented with one facial muscle or more. Seven AUs are used as possible markers for detecting confusion that has been implemented in the form of a single vector of facial action; the AUs that have been used in this work are AUs 4, 5, 6, 7, 10, 12, and 23. The database used to calculate the performance of the proposed CDS is gathered from 120 participants (91males, 29 females), between the ages of 18-45. Four types of classification algorithms are used as individuals; these classifiers are (VG-RAM), (SVM), Logistic Regression and Quadratic Discriminant classifiers. The best success rate was found when using Logistic Regression and Quadratic Discriminant. This work introduces different classification techniques to detect confusion by collecting an actual database that can be used to evaluate the performance for every CDS employing facial expressions and selecting appropriate facial features.
Highlights
Confusion detection is essential in current days; many studies reveal the vital role that confusion can play in learning, as found in Massive Open Online Course (MOOC)
Many techniques based on facial expressions extraction have been utilized for confusion detection, which used a variety of types of Action Units (AUs) as an indicator for confusion as found in [6, 7, 10, and 11]
In the decision-maker stage, four types of different classifiers are proposed: Virtual Generalizing Random Access Memory (VG-RAM), Support Vector Machine (SVM), Logistic Regression and Quadratic Discriminant classifiers, that used to differentiate between a confused person who is not confused
Summary
Confusion detection is essential in current days; many studies reveal the vital role that confusion can play in learning, as found in Massive Open Online Course (MOOC). There are other indications of confusion discussed in the previous work, which are eye movement, EEG data, EMG data, facial expression, eye gaze and the way expressing the Language and Discourse Analysis for a learner The latter one was proposed by Atapattu [3] by adapting the confusion classification technique in MOOC to identify which aspects impact the overall learning process. Most CDSs used invasive techniques mentioned earlier may give inaccurate information because it requires connecting sensors to the participant’s body, which makes the participant confused/anxious during the experiment. While this potentially can give, false misreading sensor signals leading to confusion detection miss predication. This proposed CDS is an autonomous system and can operate in unconstrained environments
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