arXiv cs.AI· Zixuan Shen, Zhihua Xia, Kaikai Gan, Peipeng Yu·· 14 小时前AI 评分31
GMoE-FTI：采用真正 MoE 机制的通用人脸模板反演方法
GMoE-FTI: A Universal Facial Template Inversion Method Adopting Genuine Mixture-of-Experts Mechanism
AI 导读
研究者提出通用人脸模板反演框架 GMoE-FTI，采用真正的 MoE 机制而非负载均衡多分支方案，可从五类分布各异的人脸模板重建身份一致的人脸。该方法分三阶段训练：先独立训练五个映射专家（ME）与匹配模块（MM），再协同推理生成兼容 StyleGAN3 的统一隐码，最后端到端联合微调。
正文 · 原文
Abstract:Face recognition systems have been ubiquitously deployed across daily scenarios. To protect user privacy, mainstream systems store facial templates rather than raw face images for identity verification. However, facial template inversion (FTI) technologies can reconstruct faces from templates, which endangers biometric privacy. Existing FTI methods only fit single type of templates, failing to reconstruct identity-consistent faces from unseen types. To tackle this limitation, we survey mainstream template extractors which can be classified into five categories according the template distribution: unit-sphere, angle-linear, strict unit-sphere, cone-shaped, anuniform hypersphere. Inspired by this observation, this paper proposes GMoE-FTI, a universal FTI framework powered by a genuine MoE mechanism that differs from load-balanced multi-branch alternatives. It follows a three-stage training strategy: (1) independently train five mapping experts (MEs) and a matching module (MM); (2) conduct collaborative inference by weighting each ME's output with MM's output, so as to generate unified latent codes compatible with pre-trained StyleGAN3; (3) perform end-to-end joint fine-tuning over all modules to identity consistency and boost cross-expert coordination. Extensive evaluations on the LFW benchmark validate the superiority of GMoE-FTI against SOTA methods. Evaluated on seven prevailing unseen template types including AdaFace, CosFace, CurricularFace, ElasticFace, SphereFace2, SphereFace-R, and TransFace, GMoE-FTI achieves an average 40% TAR improvement, with average FAPD and FAPC each reduced by 2%, verifying its universal generalization across heterogeneous templates.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33055 [cs.CV] |
| (or arXiv:2610.33055v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33055 arXiv-issued DOI via DataCite |
Submission history
From: Zixuan Shen [view email]
[v1]
Mon, 19 Oct 2026 07:31:52 UTC (1,859 KB)
来源：arXiv cs.AI · arxiv.org