发表机构
University of Luxembourg（卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
探讨合成数据集能否取代真实基准用于人脸识别评估，用24个预训练模型对12个合成数据集与7个真实基准测试，发现MorphFace和Vec2Face表现突出，结果表明精心构建的合成数据集可支持可靠比较评估，推动领域向全合成且隐私保护流程发展。
AI 中文摘要
合成人脸数据集已足以有效训练人脸识别模型，其准确率可与基于真实照片训练的模型相媲美。这一进展规避了收集真实生物特征数据的伦理和法律负担，但评估却未能跟上。即便完全基于合成图像训练的研究仍依赖真实人脸基准来衡量性能，隐私问题仅解决了一半。我们探讨合成数据集能否取代真实基准用于人脸识别评估。使用涵盖卷积和Transformer架构的24个预训练模型，对12个合成数据集与7个既定真实基准进行测试。评估涵盖生物特征验证指标、相似度得分分布、跨模型排名一致性及各数据集的潜在分布特性。合成候选数据集的基准测试保真度差异很大，但最强的两个，即MorphFace和Vec2Face，再现了真实基准的相对行为，且达成的一致水平在真实基准自身已观察到的自然分歧范围内。这些结果表明，精心构建的合成数据集可支持可靠的人脸识别比较评估，使该领域更接近用于训练和基准测试的完全合成且保护隐私的流程。
英文摘要
Synthetic face datasets have become effective enough to train face recognition models with accuracy rivaling that of models trained on real photographs. This progress sidesteps the ethical and legal burdens of collecting real biometric data, yet evaluation has not kept pace. Even studies that train entirely on synthetic images still rely on real-face benchmarks to measure performance, leaving the privacy problem only half solved. We ask whether synthetic datasets can replace real benchmarks for face recognition evaluation. We test 12 synthetic datasets against 7 established real benchmarks using 24 pre-trained models that span both convolutional and transformer architectures. Our evaluation covers biometric verification metrics, similarity score distributions, cross-model ranking consistency, and the underlying distributional properties of each dataset. Benchmarking fidelity varies widely across the synthetic candidates, but the two strongest, MorphFace and Vec2Face, reproduce the relative behavior of real benchmarks and reach agreement levels that fall within the natural disagreement already observed among the real benchmarks themselves. These results establish that well-constructed synthetic datasets can support reliable comparative evaluation for face recognition, moving the field closer to a fully synthetic and privacy-preserving pipeline for both training and benchmarking.
CommentsAccepted at IJCB 2026
