Abstract
Face identification systems operating in the ciphertext domain have garnered significant attention due to increasing privacy concerns and the potential recovery of original facial data. However, as the size of ciphertext template libraries grows, the face retrieval process becomes progressively more time-intensive. To address this challenge, we propose a novel and efficient scheme for face retrieval in the ciphertext domain, termed Privacy-Preserving Preselection for Face Identification Based on Packing (PFIP). PFIP incorporates an innovative preselection mechanism to reduce computational overhead and a packing module to enhance the flexibility of biometric systems during the enrollment stage. Extensive experiments conducted on the LFW and CASIA datasets demonstrate that PFIP preserves the accuracy of the original face recognition model, achieving a 100% hit rate while retrieving 1,000 ciphertext face templates within 300 milliseconds. Compared to existing approaches, PFIP achieves a nearly 50x improvement in retrieval efficiency.
Abstract (translated)
基于加密域中的人脸识别系统由于隐私保护需求和防止原始面部数据恢复的潜在风险而备受关注。然而，随着加密模板库大小的增长，人脸检索过程变得越来越耗时。为解决这一挑战，我们提出了一种新颖且高效的方案——基于包装（Packing）的身份认证隐私保护预选方案（PFIP），用于加密域中的面部识别。该方案集成了创新的预选择机制以减少计算开销，并引入了一个打包模块来增强生物特征系统在注册阶段的灵活性。 在LFW和CASIA数据集中进行的广泛实验表明，PFIP能够保持原始人脸识别模型的准确性，在检索1,000个加密人脸模板时实现100%命中率且耗时不超过300毫秒。与现有方法相比，PFIP在检索效率上实现了近50倍的提升。
URL
https://arxiv.org/abs/2507.02414
