2022
DOI: 10.1016/j.image.2021.116616
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Multi-task Facial Activity Patterns Learning for micro-expression recognition using Joint Temporal Local Cube Binary Pattern

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Cited by 8 publications
(1 citation statement)
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“…Later, in [31], Li et al constructed a deep local holistic network to learn features from local and global face regions. Cen et al [32] proposed joint temporal local cube binary pattern and multi-task facial activity patterns learning framework to explore the relationship between action units and emotional states. In recent years, many researchers have used deep learning to explore micro-expressions due to its excellent performance in computer vision tasks.…”
Section: Related Workmentioning
confidence: 99%
“…Later, in [31], Li et al constructed a deep local holistic network to learn features from local and global face regions. Cen et al [32] proposed joint temporal local cube binary pattern and multi-task facial activity patterns learning framework to explore the relationship between action units and emotional states. In recent years, many researchers have used deep learning to explore micro-expressions due to its excellent performance in computer vision tasks.…”
Section: Related Workmentioning
confidence: 99%