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Found 3 papers, 3 papers with code
Classifying emotions and engagement in online learning based on a single facial expression recognition neural network
It is shown that the resulting facial features can be used for fast simultaneous prediction of students’ engagement levels (from disengaged to highly engaged), individual emotions (happy, sad, etc.,) and group-level affect (positive, neutral or negative).
Ranked #5 on
Facial Expression Recognition (FER)
on AffectNet
Video-Based Frame-Level Facial Analysis of Affective Behavior on Mobile Devices Using EfficientNets
In this paper, we consider the problem of real-time video-based facial emotion analytics, namely, facial expression recognition, prediction of valence and arousal and detection of action unit points.
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks
Moreover, it is shown that the usage of our neural network as a feature extractor of facial regions in video frames and concatenation of several statistical functions (mean, max, etc.)
