Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3142
Title: Child Head Gesture Classification through Transformers
Authors: Wedasingha, N
Samarasinghe, P
Singarathnam, D
Papandrea, M
Puiatti, A
Seneviratne, L
Keywords: Head Pose Estimation
Logistic Regression
SVM
Transfer Learning
Transformer
Issue Date: 4-Nov-2022
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: N. Wedasingha, P. Samarasinghe, D. Singarathnam, M. Papandrea, A. Puiatti and L. Seneviratne, "Child Head Gesture Classification through Transformers," TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON), Hong Kong, Hong Kong, 2022, pp. 1-6, doi: 10.1109/TENCON55691.2022.9977990.
Series/Report no.: IEEE Region 10 Annual International Conference, Proceedings/TENCON;
Abstract: This paper proposes a transformer network for head pose classification (HPC) which outperforms the existing SoA for HPC. This robust model is then extended to overcome the limited child data challenge by applying transfer learning resulting in an accuracy of 95.34% for child HPC in the wild.
URI: https://rda.sliit.lk/handle/123456789/3142
ISSN: 21593442
Appears in Collections:Department of Information Technology

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