SynThermFace：通过合成数据生成增强可见光-热成像人脸识别的有限配对数据
SynThermFace: Amplifying Limited Paired Data for Visible-Thermal Face Recognition via Synthetic Data Generation
- Idiap Research Institute（伊迪亚普研究所）
- University of Lausanne (UNIL)（洛桑大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
SynThermFace利用扩散模型将有限的真实可见光-热成像配对数据扩展为大规模合成配对数据，在训练阶段生成数据并适应预训练模型，从而提升跨光谱人脸识别性能，无需测试时图像转换。
AI中文摘要：
人脸识别（FR）是一种广泛使用的生物特征认证方式，但传统模型依赖可见光谱图像，在无法捕获高质量RGB图像时性能会下降。跨光谱人脸识别通过将可见光图像与热成像等其他模态进行匹配来解决这一限制，从而在低光照、夜间和无约束条件下实现更可靠的性能。然而，配对可见光-热成像数据的稀缺限制了进展，因为大规模收集此类数据困难且成本高昂。我们提出SynThermFace，一个将有限的真实可见光-热成像监督信号扩展为更大的配对适应数据集，用于跨光谱人脸识别的框架。首先使用有限的配对可见光-热成像图像适应扩散模型，然后利用该模型从现有的真实或合成可见光人脸数据集生成大规模配对可见光-合成热成像数据。生成的配对数据用于将预训练的可见光谱人脸识别模型适应为跨光谱人脸识别（CFR）模型。与需要在测试时进行图像转换的基于合成的方法不同，所提出的方法将生成过程转移到训练阶段，并通过适应后的识别模型进行单次前向传播即可完成推理。在相同的MCXFace真实配对协议下，PACT在评估的CFR适应基线中取得了改进，从而隔离了所提出的适应目标的效果。在更大的生成配对数据集上训练PACT，相比未适应模型和真实配对PACT配置均带来了额外改进。在Tufts数据集上的跨数据库评估提供了证据，表明学习到的表示能够迁移到未见过的数据库。源代码和训练模型将公开发布。
英文摘要：
Face recognition (FR) is a widely used modality for biometric authentication, but conventional models rely on visible-spectrum imagery and degrade when high-quality RGB images cannot be captured. Cross-spectral face recognition addresses this limitation by matching visible images with other modalities such as thermal imagery, enabling more reliable performance in low-light, nighttime, and unconstrained conditions. However, progress is limited by the scarcity of paired visible-thermal data, which is difficult and costly to collect at scale. We propose SynThermFace, a framework that amplifies limited real visible-thermal supervision into larger paired adaptation datasets for cross-spectral face recognition. A diffusion model is first adapted using a limited set of paired visible--thermal images and then used to generate large-scale paired visible--synthetic thermal data from existing real or synthetic visible face datasets. The generated pairs are used to adapt a pretrained visible-spectrum face recognition model into a CFR model. Unlike synthesis-based approaches that require image translation at test time, the proposed method shifts generation to the training stage and performs inference with a single forward pass through the adapted recognition model. Under the same MCXFace real-pair protocol, PACT improves over the evaluated CFR adaptation baselines, isolating the effect of the proposed adaptation objective. Training PACT on larger generated paired datasets provides additional improvements over both the unadapted model and the real-pair PACT configuration. Cross-database evaluation on the Tufts dataset provides evidence that the learned representation transfers to an unseen database. The source code and trained models will be made publicly available.
