Fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks
With the incidence of the Covid-19 pandemic, institutions have adopted online learning as the main lessondelivery channel. A common criticism of online learning is that sensing of learners’ affective states such asengagement is lacking which degrades the quality of teaching. In this study, we propos...
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sg-smu-ink.sis_research-81602023-08-04T06:01:02Z Fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks FWA, Hua Leong With the incidence of the Covid-19 pandemic, institutions have adopted online learning as the main lessondelivery channel. A common criticism of online learning is that sensing of learners’ affective states such asengagement is lacking which degrades the quality of teaching. In this study, we propose automatic sensing of learners’ affective states in an online setting with web cameras capturing their facial landmarks and head poses. We postulate that the sparsely connected facial landmarks can be modelled using a Graph Neural Network. Using the publicly available in the wild DAiSEE dataset, we modelled both the spatial and temporal dimensions of the facial videos with a deep learning architecture consisting of Graph Attention Networks and Gated Recurrent Units. The ablation study confirmed that the differencing of consecutive frames of facial landmarks and the addition of head poses enhance the detection performance. The results further demonstrated that the model performed well in comparison with other models and more importantly, is suited for implementation on mobile devices with its low computational requirements. 2022-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7157 info:doi/10.5220/0010921200003182 https://ink.library.smu.edu.sg/context/sis_research/article/8160/viewcontent/paper10_emotion_detection_STGAN_camera.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University spatial temporal affective states facial landmarks graph attention network gated recurrent unit Databases and Information Systems |
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spatial temporal affective states facial landmarks graph attention network gated recurrent unit Databases and Information Systems FWA, Hua Leong Fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks |
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With the incidence of the Covid-19 pandemic, institutions have adopted online learning as the main lessondelivery channel. A common criticism of online learning is that sensing of learners’ affective states such asengagement is lacking which degrades the quality of teaching. In this study, we propose automatic sensing of learners’ affective states in an online setting with web cameras capturing their facial landmarks and head poses. We postulate that the sparsely connected facial landmarks can be modelled using a Graph Neural Network. Using the publicly available in the wild DAiSEE dataset, we modelled both the spatial and temporal dimensions of the facial videos with a deep learning architecture consisting of Graph Attention Networks and Gated Recurrent Units. The ablation study confirmed that the differencing of consecutive frames of facial landmarks and the addition of head poses enhance the detection performance. The results further demonstrated that the model performed well in comparison with other models and more importantly, is suited for implementation on mobile devices with its low computational requirements. |
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FWA, Hua Leong |
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FWA, Hua Leong |
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FWA, Hua Leong |
title |
Fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks |
title_short |
Fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks |
title_full |
Fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks |
title_fullStr |
Fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks |
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Fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks |
title_sort |
fine-grained detection of academic emotions with spatial temporal graph attention networks using facial landmarks |
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Institutional Knowledge at Singapore Management University |
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2022 |
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https://ink.library.smu.edu.sg/sis_research/7157 https://ink.library.smu.edu.sg/context/sis_research/article/8160/viewcontent/paper10_emotion_detection_STGAN_camera.pdf |
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