A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection
Fu, Wenwen (China University of Petroleum)
An, Zhihong (Tsinghua University)
Huang, Wendong (China University of Petroleum)
Sun, Haoran (China University of Petroleum)
Gong, Wenjuan (China University of Petroleum)
Gonzàlez, Jordi (Centre de Visió per Computador (Bellaterra, Catalunya))

Fecha:	2023
Resumen:	Micro-expressions reveal underlying emotions and are widely applied in political psychology, lie detection, law enforcement and medical care. Micro-expression spotting aims to detect the temporal locations of facial expressions from video sequences and is a crucial task in micro-expression recognition. In this study, the problem of micro-expression spotting is formulated as micro-expression classification per frame. We propose an effective spotting model with sliding windows called the spatio-temporal spotting network. The method involves a sliding window detection mechanism, combines the spatial features from the local key frames and the global temporal features and performs micro-expression spotting. The experiments are conducted on the CAS(ME) (Formula presented. ) database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by (Formula presented. ) for the CAS(ME) (Formula presented. ) and (Formula presented. ) for the SAMM Long Videos according to overall F-scores.
Ayudas:	Agencia Estatal de Investigación PID2020-120311RB-I00
Derechos:
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Publicado en:	Electronics, Vol. 12, Issue 18 (September 2023) , art. 3947, ISSN 2079-9292

DOI: 10.3390/electronics12183947

14 p, 1.8 MB

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