Multimodal Group Emotion Recognition In-the-Wild Towards a Privacy-Safe Non-Individual Approach
arXiv:2606.07585v1 Announce Type: cross 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 agg
The increasing prevalence of AI in public spaces necessitates solutions addressing privacy concerns in group behavior analysis, driving research towards non-individual approaches.
This research outlines a method for group emotion recognition that prioritizes privacy by avoiding individual-level surveillance, which could unlock broader adoption of AI for public safety and social analysis without infringing on personal liberties.
The ability to infer group emotions from collective signals rather than individual biometrics changes the scope and ethical implications of AI deployment in crowded environments, potentially expanding its applications while mitigating privacy risks.
- · Public safety organizations
- · Smart city developers
- · AI ethics researchers
- · Event management
- · Individual-centric surveillance technologies
- · Companies reliant on granular personal biometric data
Wider deployment of AI for crowd analysis without specific individual identification will become more socially acceptable and legally viable.
This shift could accelerate the development of 'privacy-by-design' AI systems for various collective intelligence applications.
The reduced risk of individual monitoring might lower public resistance to AI presence in public spaces, leading to unforeseen applications in urban planning or public welfare.
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Read at arXiv cs.AI