Journal of Electronic Science and Technology, Volume. 22, Issue 3, 100260(2024)
Global-local combined features to detect pain intensity from facial expression images with attention mechanism
Fig. 1. Overall pre-processing pipeline: (a) original image, (b) facial localization, (c) facial alignment, and (d) facial cropping.
Fig. 3. Sample frames and their corresponding PSPI scores at the UNBC-McMaster Shoulder Pain database.
Fig. 4. Number of pictures per PSPI code class at the UNBC-McMaster Shoulder Pain database.
Fig. 6. Samples of attention map from “No Pain” to “Strong Pain” by different models at the UNBC-McMaster Shoulder Pain database.
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Jiang Wu, Yi Shi, Shun Yan, Hong-Mei Yan. Global-local combined features to detect pain intensity from facial expression images with attention mechanism[J]. Journal of Electronic Science and Technology, 2024, 22(3): 100260
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Received: Sep. 2, 2023
Accepted: May. 21, 2024
Published Online: Oct. 11, 2024
The Author Email: Yan Hong-Mei (hmyan@uestc.edu.cn)