Benchmarking deep Facial Expression Recognition: An extensive protocol with balanced dataset in the wild

Tutuianu, Gianmarco Ipinze; Liu, Yang; Alamäki, Ari; Kauttonen, Janne

doi:10.1016/j.engappai.2024.108983

Engineering Applications of Artificial Intelligence

2024

DOI: 10.1016/j.engappai.2024.108983

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Benchmarking deep Facial Expression Recognition: An extensive protocol with balanced dataset in the wild

Gianmarco Ipinze Tutuianu,

Yang Liu,

Ari Alamäki

et al.

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Interactions for Socially Shared Regulation in Collaborative Learning: An Interdisciplinary Multimodal Dataset

Li,

Liu,

Nguyen

et al. 2024

ACM Trans. Interact. Intell. Syst.

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Socially shared regulation plays a pivotal role in the success of collaborative learning. However, evaluating socially shared regulation of learning (SSRL) proves challenging due to the dynamic and infrequent cognitive and socio-emotional interactions, which constitute the focal point of SSRL. To address this challenge, this paper gathers interdisciplinary researchers to establish a multi-modal dataset with cognitive and socio-emotional interactions for SSRL study. Firstly, to induce cognitive and socio-emotional interactions, learning science researchers designed a special collaborative learning task with regulatory trigger events among triadic people for the SSRL study. Secondly, this dataset includes various modalities like video, Kinect data, audio, and physiological data (accelerometer, EDA, heart rate) from 81 high school students in 28 groups, offering a comprehensive view of the SSRL process. Thirdly, three-level verbal interaction annotations and non-verbal interactions including facial expression, eye gaze, gesture, and posture are provided, which could further contribute to interdisciplinary fields such as computer science, sociology, and education. In addition, comprehensive analysis verifies the dataset’s effectiveness. As far as we know, this is the first multimodal dataset for studying SSRL among triadic group members.

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Interactions for Socially Shared Regulation in Collaborative Learning: An Interdisciplinary Multimodal Dataset

Li,

Liu,

Nguyen

et al. 2024

ACM Trans. Interact. Intell. Syst.

View full text Add to dashboard Cite

show abstract

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Benchmarking deep Facial Expression Recognition: An extensive protocol with balanced dataset in the wild

Cited by 1 publication

References 30 publications

Interactions for Socially Shared Regulation in Collaborative Learning: An Interdisciplinary Multimodal Dataset

Interactions for Socially Shared Regulation in Collaborative Learning: An Interdisciplinary Multimodal Dataset

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