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

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.

Weakly-Supervised Video Anomaly Detection with MTDA-Net

“…However, with the popularity of monitoring systems and the large increase in monitoring equipment, manual monitoring is increasingly difficult to meet the growing demand of practical applications. Along with the huge progress of deep neural networks in video behavior recognition [1][2][3], using neural networks to efficiently and quickly perform anomaly behavior detection has become a very good method.…”