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.

提出了一种高效的隐私保护多生物特征识别系统，通过利用来自不同类型生物特征的频繁二进制模式中包含的低类内变异属性，设计了一种多生物特征分箱方案，实验结果表明，该多生物特征识别系统可以将计算工作量降低约 57%（索引最多三种生物特征类型）和 53%（索引最多两种生物特征类型），同时提高了基准生物特征系统在高安全阈值下的生物特征性能。