@Article{WenwenFu2023,
author="Wenwen Fu
and Zhihong An
and Wendong Huang
and Haoran Sun
and Wenjuan Gong
and Jordi Gonzalez",
title="A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection",
journal="Electronics",
year="2023",
volume="12",
number="18",
pages="3947",
optkeywords="micro-expression spotting",
optkeywords="sliding window",
optkeywords="key frame extraction",
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.",
optnote="ISE",
optnote="exported from refbase (http://158.109.8.37/show.php?record=3864), last updated on Tue, 06 Feb 2024 13:36:33 +0100",
opturl="https://doi.org/10.3390/electronics12183947"
}