# RFOP: Rethinking Fusion and Orthogonal Projection for Face-Voice Association

arXiv: https://arxiv.org/abs/2512.02860 · HTML: https://mlsanity.com/paper/2512.02860/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 12 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Seeing Voices and Hearing Faces: Cross-modal biometric matching](https://mlsanity.com/paper/1804.00326/) (2018)
- [Learnable PINs: Cross-Modal Embeddings for Person Identity](https://mlsanity.com/paper/1805.00833/) (2018)
- [Fusion and Orthogonal Projection for Improved Face-Voice Association](https://mlsanity.com/paper/2112.10483/) (2021)
- [Cross-modal Speaker Verification and Recognition: A Multilingual Perspective](https://mlsanity.com/paper/2004.13780/) (2020)
- [Deep Latent Space Learning for Cross-modal Mapping of Audio and Visual Signals](https://mlsanity.com/paper/1909.08685/) (2019)
- [Single-branch Network for Multimodal Training](https://mlsanity.com/paper/2303.06129/) (2023)
- [Speaker Recognition in Realistic Scenario Using Multimodal Data](https://mlsanity.com/paper/2302.13033/) (2023)
- [PAEFF: Precise Alignment and Enhanced Gated Feature Fusion for Face-Voice Association](https://mlsanity.com/paper/2505.17002/) (2025)
- [Face-voice Association in Multilingual Environments (FAME) 2026 Challenge Evaluation Plan](https://mlsanity.com/paper/2508.04592/) (2025)

## Built on by (most cited first, 1 of 1)

- [Linking Faces and Voices Across Languages: Insights from the FAME 2026 Challenge](https://mlsanity.com/paper/2512.20376/) (2025)

## Abstract (arXiv)

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.