LBFTI：基于层的面部模板反向技术用于身份保持的细粒度面部重建
LBFTI: Layer-Based Facial Template Inversion for Identity-Preserving Fine-Grained Face Reconstruction
AI总结：
本文提出LBFTI方法，通过分解面部为前景、中景和背景三层，利用专用生成器和三阶段训练策略实现身份保持的细粒度面部重建，实验表明其在机器认证和人类感知相似性上均优于现有方法。
摘要：
在人脸识别系统中，面部模板因其符合数据最小化原则而被广泛用于身份认证。然而，面部模板反向技术通过从模板中重建面部图像导致严重的隐私泄露风险。本文提出一种基于层的面部模板反向（LBFTI）方法，用于重建身份保持的细粒度面部图像。我们的方案将面部图像分解为三个层次：前景层次（包括眉毛、眼睛、鼻子和嘴巴）、中景层次（皮肤）和背景层次（其他部分）。LBFTI利用专用生成器生成这些层次，采用严格三阶段训练策略：（1）独立精细化生成前景和中景层次；（2）融合前景和中景层次并利用模板二次注入生成完整的全景面部图像并添加背景层次；（3）联合微调所有模块以优化层间协调性和身份一致性。实验表明，我们的LBFTI不仅在机器认证性能上优于现有最先进方法，其在机器认证性能上提升了25.3%的TAR，而且在人类感知相似性上也表现更好，这通过定量指标和问卷调查得到了验证。
英文摘要：
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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