A Survey of Deep Learning for Group-level Emotion Recognition

TL;DR

This paper provides the first comprehensive review of deep learning techniques for group-level emotion recognition, covering datasets, methods, challenges, and future directions in the field.

Contribution

It introduces a new taxonomy for deep learning approaches in GER and offers a detailed overview of recent advancements and challenges.

Findings

Deep learning significantly improves GER performance.

A new taxonomy categorizes GER methods based on DL.

Identifies key challenges and future research directions.

Abstract

With the advancement of artificial intelligence (AI) technology, group-level emotion recognition (GER) has emerged as an important area in analyzing human behavior. Early GER methods are primarily relied on handcrafted features. However, with the proliferation of Deep Learning (DL) techniques and their remarkable success in diverse tasks, neural networks have garnered increasing interest in GER. Unlike individual's emotion, group emotions exhibit diversity and dynamics. Presently, several DL approaches have been proposed to effectively leverage the rich information inherent in group-level image and enhance GER performance significantly. In this survey, we present a comprehensive review of DL techniques applied to GER, proposing a new taxonomy for the field cover all aspects of GER based on DL. The survey overviews datasets, the deep GER pipeline, and performance comparisons of the state-of-the-art methods past decade. Moreover, it summarizes and discuss the fundamental approaches and advanced developments for each aspect. Furthermore, we identify outstanding challenges and suggest potential avenues for the design of robust GER systems. To the best of our knowledge, thus survey represents the first comprehensive review of deep GER methods, serving as a pivotal references for future GER research endeavors.