Multimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach
This doctoral thesis proposes two privacy-preserving frameworks for group emotion recognition (GER) using collective audio-video signals instead of individual cues. The first uses cross-attention multimodal fusion with Frames Attention Pooling (FAP), the second (VE-MD) learns a shared latent space for emotion classification and structural representation prediction. Results show competitive performance without individual features.
[2606.07585] Multimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach
[Submitted on 27 May 2026]
Title:Multimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach
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Abstract:This thesis addresses group emotion recognition (GER) in-the-wild with a focus on privacy preservation. Unlike traditional emotion recognition methods that rely on individual-level cues such as face, gaze, or voice analysis, this work uses collective audio-video signals to infer emotions at the group level, reducing risks of individual monitoring and surveillance. Two complementary frameworks are proposed. The first is a cross-attention multimodal architecture for audio-video fusion, combined with Frames Attention Pooling (FAP) for temporal aggregation. It is supported by synthetic data augmentation and validated through ablation studies, demonstrating robustness in real-world GER conditions. The second framework, Variational Encoder Multi-Decoder (VE-MD), learns a shared latent space for emotion classification and structural representation prediction, including body and face cues. Two decoding strategies, DETR-based and heatmap-based, are explored to analyze the role of structural representations in group and individual settings. The thesis makes three main contributions: it clarifies the role of multimodality and structural cues in group-level affective computing; introduces two architectures for privacy-preserving multimodal GER; and shows that competitive performance can be achieved without using individual features as input data.
Comments: Doctoral thesis
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.07585 [cs.CV]
(or arXiv:2606.07585v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.07585
arXiv-issued DOI via DataCite
Submission history
From: Anderson Augusma [view email] [v1] Wed, 27 May 2026 16:36:58 UTC (7,560 KB)
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