TY - GEN
T1 - Emotion Detection from Facial Expression in Online Learning Through Using Synthetic Image Generation
AU - Kabir, Md Rayhan
AU - Dewan, M. Ali Akber
AU - Lin, Fuhua
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Understanding students’ educational emotion is important for learning process, however, it is challenging to detect in an online learning environment. Deep learning architectures show excellent performance for emotion detection from facial expressions; however, their complexity and computational requirements limit their deployment on students’ edge devices. Additionally, the availability and the size of the datasets for detecting educational emotions are scarce. In this study, we propose a lightweight deep learning model based on MobileNet architecture which is deployable in students’ edge devices for educational emotion detection. We also propose a generative adversarial network based synthetic image generation technique to address the challenges of scarcity of the dataset. This framework is compared with the state-of-the-art models, where it demonstrated competitive performance while making it suitable for the edge devices for the educational emotion detection, and additionally, the use of the synthetic dataset further contributes to improve the performance of the proposed model.
AB - Understanding students’ educational emotion is important for learning process, however, it is challenging to detect in an online learning environment. Deep learning architectures show excellent performance for emotion detection from facial expressions; however, their complexity and computational requirements limit their deployment on students’ edge devices. Additionally, the availability and the size of the datasets for detecting educational emotions are scarce. In this study, we propose a lightweight deep learning model based on MobileNet architecture which is deployable in students’ edge devices for educational emotion detection. We also propose a generative adversarial network based synthetic image generation technique to address the challenges of scarcity of the dataset. This framework is compared with the state-of-the-art models, where it demonstrated competitive performance while making it suitable for the edge devices for the educational emotion detection, and additionally, the use of the synthetic dataset further contributes to improve the performance of the proposed model.
KW - Educational emotion detection
KW - facial expression recognition
KW - lightweight architecture
KW - neural network
KW - online learning
UR - http://www.scopus.com/inward/record.url?scp=85196269178&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-60611-3_15
DO - 10.1007/978-3-031-60611-3_15
M3 - Published Conference contribution
AN - SCOPUS:85196269178
SN - 9783031606137
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 202
EP - 216
BT - Artificial Intelligence in HCI - 5th International Conference, AI-HCI 2024, Held as Part of the 26th HCI International Conference, HCII 2024, Proceedings
A2 - Degen, Helmut
A2 - Ntoa, Stavroula
T2 - 5th International Conference on Artificial Intelligence in HCI, AI-HCI 2024, held as part of the 26th HCI International Conference, HCII 2024
Y2 - 29 June 2024 through 4 July 2024
ER -