SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset

TL;DR

This paper introduces the SAMM Long Videos dataset, a comprehensive resource of long videos with spontaneous micro- and macro-expressions, along with a facial expression spotting method that outperforms existing baselines.

Contribution

The paper presents a new long videos dataset with detailed annotations for micro- and macro-expressions and demonstrates improved facial expression spotting performance.

Findings

The dataset contains 147 long videos with 343 macro- and 159 micro-expressions.

The proposed spotting method achieved an F1-score of 0.3299, surpassing the baseline.

The dataset is FACS-coded with detailed Action Units for detailed analysis.

Abstract

With the growth of popularity of facial micro-expressions in recent years, the demand for long videos with micro- and macro-expressions remains high. Extended from SAMM, a micro-expressions dataset released in 2016, this paper presents SAMM Long Videos dataset for spontaneous micro- and macro-expressions recognition and spotting. SAMM Long Videos dataset consists of 147 long videos with 343 macro-expressions and 159 micro-expressions. The dataset is FACS-coded with detailed Action Units (AUs). We compare our dataset with Chinese Academy of Sciences Macro-Expressions and Micro-Expressions (CAS(ME)2) dataset, which is the only available fully annotated dataset with micro- and macro-expressions. Furthermore, we preprocess the long videos using OpenFace, which includes face alignment and detection of facial AUs. We conduct facial expression spotting using this dataset and compare it with the baseline of MEGC III. Our spotting method outperformed the baseline result with F1-score of 0.3299.