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Reference | Emotion classifier | Type of emotions | Feature extraction | Results | Limitation | Real time |
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[97] | Long-term recurrent convolutional network | Boredom, confusion, engagement, and frustration | N/A | Top-1 accuracy: 94.6% | Low illumination and lack of frontal pose | No |
[35] | Deep belief network (DBN) | Two-level engagement and three-level engagement | Viola-Jones algorithm | Accuracy: (i) Two-level: 90.89% (ii) Three-level: 87.25% | Unknown direct correlation between engagement and actual task performance | No |
[46] | CNN | Confusion, distraction, enjoyment, fatigue, neutral | VGG16 | Accuracy: 91.60% | Limited sample number | No |
[47] | BERN | Boredom, confusion, engagement, and frustration | OpenFace | Top-1 accuracy: 60% for four classification model | Requires a large amount of training data and a long training time | Yes |
[52] | LSTM | Boredom and frustration | MTCNN library—face detection & cropping | Accuracy compared to the EmotioNet model: (i) Boredom: +16.26% (ii) Frustration -2.42% | (i) Decreased accuracy for frustration emotion (ii) Involved negative emotions only | No |
[61] | CNN | Boredom, confusion, engagement, and frustration | CNN+pose estimator | Accuracy: 53.4% | Show a low recognition rate for the frustration when using the DAiSEE dataset | Yes |
[64] | Deep facial spatiotemporal network (DFSTN) | Engagement level: very low, low, high, and very high | MTCNN | Accuracy: 58.84% | (i) Low detection accuracy (ii) Data deficiencies and data imbalances | No |
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