RFOP: Rethinking Fusion and Orthogonal Projection for Face-Voice Association
Abdul Hannan, Furqan Malik, Hina Jabbar, Syed Suleman Sadiq, Mubashir Noman
TL;DR
This paper presents RFOP, a novel approach for face-voice association in multilingual environments, emphasizing fusion and orthogonal projection to improve semantic relevance, achieving competitive results in the FAME 2026 challenge.
Contribution
The paper introduces RFOP, a new method that enhances face-voice association by focusing on semantic information through fusion and orthogonal projection techniques.
Findings
Achieved 33.1% EER on English-German face-voice data
Ranked 3rd in the FAME 2026 challenge
Effective focus on relevant semantic information improves performance
Abstract
Face-voice association in multilingual environment challenge 2026 aims to investigate the face-voice association task in multilingual scenario. The challenge introduces English-German face-voice pairs to be utilized in the evaluation phase. To this end, we revisit the fusion and orthogonal projection for face-voice association by effectively focusing on the relevant semantic information within the two modalities. Our method performs favorably on the English-German data split and ranked 3rd in the FAME 2026 challenge by achieving the EER of 33.1.
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Taxonomy
TopicsFace recognition and analysis · Emotion and Mood Recognition · Face Recognition and Perception