基于频繁二进制模式的隐私保护多生物特征索引
Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns
AI总结：
针对现有隐私保护生物特征模板方案无法高效索引、计算效率低的问题，提出基于频繁二进制模式的多生物特征分箱方案，实现隐私保护的同时大幅降低计算负载并提升高安全阈值下的识别性能。
摘要：
开发可确保已注册受试者隐私保护的大规模身份识别系统是一项重大挑战。通过纳入高效的多生物特征解决方案来提供互操作性和可用性的生物特征部署是近期的需求。在隐私保护的背景下，过去已经提出了多种模板保护方案。然而，这些方案似乎不适用于生物特征识别系统中的索引（即减少工作负载）。更具体地说，它们已被用于执行穷举搜索的识别系统中，导致计算效率下降。为了克服这些限制，我们提出了一种高效的隐私保护多生物特征识别系统，该系统可检索受保护的深度可撤销模板，且与生物特征类型及生物特征模板保护方案无关。为此，我们设计了一种多生物特征分箱方案，利用从不同类型生物特征中提取的频繁二进制模式所具有的低类内变化特性。使用基于最先进的深度神经网络（DNN）的嵌入提取器在公开数据库上报告的实验结果表明，该受保护的多生物特征识别系统可将计算工作负载降低至约57%（索引最多三种生物特征）和53%（索引最多两种生物特征），同时在高安全阈值下提升了基线生物特征系统的生物特征性能。所提出的多生物特征索引方法的源代码以及构建的多生物特征数据集，将在本文被接受后提供给研究界。
英文摘要：
The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multi-biometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes seem inadequate for indexing (workload reduction) in biometric identification systems. More specifically, they have been used in identification systems that perform exhaustive searches, leading to a degradation of computational efficiency. To overcome these limitations, we propose an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates and is agnostic with respect to biometric characteristics and biometric template protection schemes. To this end, a multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics. Experimental results reported on publicly available databases using state-of-the-art Deep Neural Network (DNN)-based embedding extractors show that the protected multi-biometric identification system can reduce the computational workload to approximately 57\% (indexing up to three types of biometric characteristics) and 53% (indexing up to two types of biometric characteristics), while simultaneously improving the biometric performance of the baseline biometric system at the high-security thresholds. The source code of the proposed multi-biometric indexing approach together with the composed multi-biometric dataset, will be made available to the research community once the article is accepted.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
