The OMG-Emotion Behavior Dataset

TL;DR

The OMG-Emotion Behavior Dataset is a novel multimodal corpus capturing natural, gradual emotional expressions over time, designed to advance emotion recognition research with a new annotation strategy and baseline experiments.

Contribution

It introduces a new multimodal dataset with gradual annotations based on evolving emotion expressions, collected from YouTube videos, and provides a standardized evaluation protocol.

Findings

Dataset captures natural emotion transitions over time

Baseline experiments establish standard benchmarks

Dataset facilitates research on continuous emotion recognition

Abstract

This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion) by which we hope to foster long-emotion classification using neural models for the benefit of the IJCNN community. The proposed corpus has as the novelty the data collection and annotation strategy based on emotion expressions which evolve over time into a specific context. Different from other corpora, we propose a novel multimodal corpus for emotion expression recognition, which uses gradual annotations with a focus on contextual emotion expressions. Our dataset was collected from Youtube videos using a specific search strategy based on restricted keywords and filtering which guaranteed that the data follow a gradual emotion expression transition, i.e. emotion expressions evolve over time in a natural and continuous fashion. We also provide an experimental protocol and a series of unimodal baseline experiments which can be used to evaluate deep and recurrent neural models in a fair and standard manner.