Micro-expression spotting: A new benchmark

TL;DR

This paper introduces a new challenging benchmark dataset and evaluation protocol for micro-expression spotting, addressing current limitations and providing baseline results for future research in automatic ME analysis.

Contribution

The paper presents a new challenging dataset, the SMIC-E-Long, a standardized evaluation protocol, and baseline experiments for micro-expression spotting.

Findings

The SMIC-E-Long database offers a more challenging benchmark for ME spotting.

A standardized evaluation protocol enables fair comparison of methods.

Baseline results are established using handcrafted and deep learning approaches.

Abstract

Micro-expressions (MEs) are brief and involuntary facial expressions that occur when people are trying to hide their true feelings or conceal their emotions. Based on psychology research, MEs play an important role in understanding genuine emotions, which leads to many potential applications. Therefore, ME analysis has become an attractive topic for various research areas, such as psychology, law enforcement, and psychotherapy. In the computer vision field, the study of MEs can be divided into two main tasks, spotting and recognition, which are used to identify positions of MEs in videos and determine the emotion category of the detected MEs, respectively. Recently, although much research has been done, no fully automatic system for analyzing MEs has yet been constructed on a practical level for two main reasons: most of the research on MEs only focuses on the recognition part, while abandoning the spotting task; current public datasets for ME spotting are not challenging enough to support developing a robust spotting algorithm. The contributions of this paper are threefold: (1) we introduce an extension of the SMIC-E database, namely the SMIC-E-Long database, which is a new challenging benchmark for ME spotting; (2) we suggest a new evaluation protocol that standardizes the comparison of various ME spotting techniques; (3) extensive experiments with handcrafted and deep learning-based approaches on the SMIC-E-Long database are performed for baseline evaluation.