Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods
Xiaobai Li, Xiaopeng Hong, Antti Moilanen, Xiaohua Huang, Tomas, Pfister, Guoying Zhao, Matti Pietik\"ainen

TL;DR
This paper introduces a novel, training-free method for spontaneous micro-expression spotting, an advanced recognition framework outperforming previous methods, and an integrated system that surpasses human performance in ME analysis.
Contribution
The paper presents the first spontaneous ME spotting method, an improved recognition framework, and an automatic analysis system combining both tasks, advancing the field significantly.
Findings
Proposed a training-free spontaneous ME spotting method.
Achieved superior recognition accuracy on SMIC and CASMEII databases.
Outperformed humans in ME recognition and matched human performance in spotting.
Abstract
Micro-expressions (MEs) are rapid, involuntary facial expressions which reveal emotions that people do not intend to show. Studying MEs is valuable as recognizing them has many important applications, particularly in forensic science and psychotherapy. However, analyzing spontaneous MEs is very challenging due to their short duration and low intensity. Automatic ME analysis includes two tasks: ME spotting and ME recognition. For ME spotting, previous studies have focused on posed rather than spontaneous videos. For ME recognition, the performance of previous studies is low. To address these challenges, we make the following contributions: (i)We propose the first method for spotting spontaneous MEs in long videos (by exploiting feature difference contrast). This method is training free and works on arbitrary unseen videos. (ii)We present an advanced ME recognition framework, which…
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