Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning

TL;DR

This paper introduces an uncertainty-aware learning approach for group-level emotion recognition that models individual uncertainties with stochastic embeddings, improving robustness in unconstrained environments.

Contribution

It proposes a novel uncertainty-aware learning method that explicitly models individual uncertainties with stochastic embeddings for more robust group emotion recognition.

Findings

Improved accuracy across multiple datasets.

Enhanced robustness against noise and occlusion.

Effective integration of face, object, and scene cues.

Abstract

Group-level emotion recognition (GER) is an inseparable part of human behavior analysis, aiming to recognize an overall emotion in a multi-person scene. However, the existing methods are devoted to combing diverse emotion cues while ignoring the inherent uncertainties under unconstrained environments, such as congestion and occlusion occurring within a group. Additionally, since only group-level labels are available, inconsistent emotion predictions among individuals in one group can confuse the network. In this paper, we propose an uncertainty-aware learning (UAL) method to extract more robust representations for GER. By explicitly modeling the uncertainty of each individual, we utilize stochastic embedding drawn from a Gaussian distribution instead of deterministic point embedding. This representation captures the probabilities of different emotions and generates diverse predictions through this stochasticity during the inference stage. Furthermore, uncertainty-sensitive scores are adaptively assigned as the fusion weights of individuals' face within each group. Moreover, we develop an image enhancement module to enhance the model's robustness against severe noise. The overall three-branch model, encompassing face, object, and scene component, is guided by a proportional-weighted fusion strategy and integrates the proposed uncertainty-aware method to produce the final group-level output. Experimental results demonstrate the effectiveness and generalization ability of our method across three widely used databases.