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DC Field | Value | Language |
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dc.contributor.author | Wedasingha, N | - |
dc.contributor.author | Samarasinghe, P | - |
dc.contributor.author | Singarathnam, D | - |
dc.contributor.author | Papandrea, M | - |
dc.contributor.author | Puiatti, A | - |
dc.contributor.author | Seneviratne, L | - |
dc.date.accessioned | 2023-01-23T10:54:32Z | - |
dc.date.available | 2023-01-23T10:54:32Z | - |
dc.date.issued | 2022-11-04 | - |
dc.identifier.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. | en_US |
dc.identifier.issn | 21593442 | - |
dc.identifier.uri | https://rda.sliit.lk/handle/123456789/3142 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.ispartofseries | IEEE Region 10 Annual International Conference, Proceedings/TENCON; | - |
dc.subject | Head Pose Estimation | en_US |
dc.subject | Logistic Regression | en_US |
dc.subject | SVM | en_US |
dc.subject | Transfer Learning | en_US |
dc.subject | Transformer | en_US |
dc.title | Child Head Gesture Classification through Transformers | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/TENCON55691.2022.9977990 | en_US |
Appears in Collections: | Department of Information Technology |
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File | Description | Size | Format | |
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Child_Head_Gesture_Classification_through_Transformers.pdf Until 2050-12-31 | 780.84 kB | Adobe PDF | View/Open Request a copy |
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