AI 中文总结
研究生物特征模板保护问题，提出多重置换标签分类器编码方法，通过特定分类器标签置换生成中间模板，经随机化和哈希处理实现精确匹配验证，在多数据集上性能良好，具备多种安全特性。
AI 中文摘要
生物特征模板保护（BTP）必须在容忍类内变化的同时保护存储的模板。现有方法存在潜在问题，如保留可利用的相似性结构、引入辅助数据风险等。身份分类适合此需求，基于此提出多重置换标签分类器编码（MPLCE）。通过特定分类器的标签置换，为每个身份在多个分类器上分配不同标签，编码并连接预测标签形成中间模板，经随机化和哈希处理实现精确匹配验证。使用特定模态分类器，MPLCE在多模态上保持相同模板生成和保护过程。在四个面部和两个虹膜数据集上取得有竞争力的性能，安全分析和攻击评估支持其在威胁模型下的不可逆性、可撤销性和不可链接性。
英文摘要
Biometric template protection (BTP) must secure stored templates while tolerating intra-class variations. Existing methods rely on protected-domain similarity matching, error correction, or predefined-template mappings, potentially retaining exploitable similarity structures, introducing helper-data risks, depending on artificial targets, or coupling protection to specific modalities. Storing only cryptographic hash digests eliminates directly comparable representations and conceals pre-hash templates, but hash-based exact-match verification requires genuine samples to generate identical intermediate templates before hashing. Identity classification is naturally suited to this requirement because it maps variable biometric samples to stable and discriminative identity-level outputs. Based on this insight, we propose Multiple Permuted-Label Classifier Encoding (MPLCE). Through classifier-specific label permutations, MPLCE assigns each identity different labels across multiple classifiers. The predicted labels are encoded and concatenated to form an intermediate template, preventing repeated encodings of a single identity label and enlarging the effective candidate space while preserving classification consistency. The template is randomized with an application-specific XOR string and cryptographically hashed, enabling exact-match verification without error correction codes or biometric-dependent helper data. Using modality-specific classifiers, MPLCE retains the same template generation and protection procedure across modalities. On four face and two iris datasets, MPLCE achieves competitive performance, including a GAR of 98.61\% at a FAR of 5.51\(\times\)10\textsuperscript{-5}\% on YTF and a GAR of 99.10\% at a FAR of 0.00\% on CASIA-Iris-Lamp. Security analyses and attack evaluations support its irreversibility, revocability, and unlinkability under the threat model.
