通过分层生成技术，从人脸模板重建高保真细节人脸图像。
LBFTI: Layer-Based Facial Template Inversion for Identity-Preserving Fine-Grained Face Reconstruction
- 将人脸分为前景、中景、背景三层，分别用专用生成器重建。
- 在机器认证上提升25.3%的识别率，人眼感知相似度更高。
- 适合关注隐私泄露与人脸重建安全的研究者参考。
在人脸识别系统中，人脸模板因符合数据最小化原则而被广泛用于身份认证。然而，人脸模板逆向技术已构成严重隐私泄露风险，可从模板重建出完整人脸。本文提出一种分层人脸模板逆向方法（LBFTI），将人脸图像分解为三部分：前景层（眉毛、眼睛、鼻子、嘴巴）、中景层（皮肤）和背景层（其他部分）。LBFTI采用专用生成器分别生成各层，并实施严格的三阶段训练策略：（1）独立优化前景与中景层生成；（2）融合前后两层并注入模板信息，生成带背景的完整人脸图像；（3）联合微调所有模块，优化层间协同与身份一致性。实验表明，该方法在机器认证性能上超越现有最佳方法，误报率降低25.3%，且在人类感知相似度方面表现更优，经量化指标与问卷调查验证。
原文摘要 · Abstract (English)
In face recognition systems, facial templates are widely adopted for identity authentication due to their compliance with the data minimization principle. However, facial template inversion technologies have posed a severe privacy leakage risk by enabling face reconstruction from templates. This paper proposes a Layer-Based Facial Template Inversion (LBFTI) method to reconstruct identity-preserving fine-grained face images. Our scheme decomposes face images into three layers: foreground layers (including eyebrows, eyes, nose, and mouth), midground layers (skin), and background layers (other parts). LBFTI leverages dedicated generators to produce these layers, adopting a rigorous three-stage training strategy: (1) independent refined generation of foreground and midground layers, (2) fusion of foreground and midground layers with template secondary injection to produce complete panoramic face images with background layers, and (3) joint fine-tuning of all modules to optimize inter-layer coordination and identity consistency. Experiments demonstrate that our LBFTI not only outperforms state-of-the-art methods in machine authentication performance, with a 25.3% improvement in TAR, but also achieves better similarity in human perception, as validated by both quantitative metrics and a questionnaire survey.
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