Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware
  Learning
Qing Zhu Qirong Mao Jialin Zhang Xiaohua Huang Wenming Zheng
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.