%0 Journal Article
%T A Spatio-Temporal Spotting Network with Sliding Windows for Micro-Expression Detection
%A Wenwen Fu
%A Zhihong An
%A Wendong Huang
%A Haoran Sun
%A Wenjuan Gong
%A Jordi Gonzalez
%J Electronics
%D 2023
%V 12
%N 18
%F Wenwen Fu2023
%O ISE
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3864), last updated on Tue, 06 Feb 2024 13:36:33 +0100
%X 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.
%K micro-expression spotting
%K sliding window
%K key frame extraction
%U https://doi.org/10.3390/electronics12183947
%P 3947