Local Temporal Pattern and Data Augmentation for Spotting Micro-Expressions

Micro-expressions (MEs) are very important nonverbal communication clues. However, due to their local and short nature, spotting them is challenging. In this article, we address this problem by using a dedicated local and temporal pattern (LTP) of facial movement. This pattern has a specific shape (...

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Veröffentlicht in:	IEEE transactions on affective computing 2023-01, Vol.14 (1), p.811-822
Hauptverfasser:	Li, Jingting, Soladie, Catherine, Seguier, Renaud
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Data augmentation Data mining Feature extraction Hammerstein model local temporal pattern Machine learning Machine learning algorithms Magnetic heads Micro-expression Muscles Outliers (statistics) spotting Training
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