A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection

Wenwen Fu, Zhihong An, Wendong Huang, Haoran Sun, Wenjuan Gong, Jordi Gonzàlez

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

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)2
database and the SAMM Long Videos database, and the results demonstrate that the proposed method outperforms the state-of-the-art method by 30.58%
for the CAS(ME)2
and 23.98%
for the SAMM Long Videos according to overall F-scores
Original languageEnglish
Article number3947
Number of pages14
JournalElectronics
Volume12
DOIs
Publication statusPublished - 19 Sept 2023

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