Modern face recognition systems utilize deep neural networks to extract
salient features from a face. These features denote embeddings in latent space
and are often stored as templates in a face recognition system. These
embeddings are susceptible to data leakage and, in some cases, can even be used
to reconstruct the original face image. To prevent compromising identities,
template protection schemes are commonly employed. However, these schemes may
still not prevent the leakage of soft biometric information such as age, gender
and race. To alleviate this issue, we propose a novel technique that combines
Fully Homomorphic Encryption (FHE) with an existing template protection scheme
known as PolyProtect. We show that the embeddings can be compressed and
encrypted using FHE and transformed into a secure PolyProtect template using
polynomial transformation, for additional protection. We demonstrate the
efficacy of the proposed approach through extensive experiments on multiple
datasets. Our proposed approach ensures irreversibility and unlinkability,
effectively preventing the leakage of soft biometric attributes from face
embeddings without compromising recognition accuracy.

我们提出了一种新颖的技术，结合了全同态加密（FHE）和现有的模板保护方案 PolyProtect，通过多项式变换将嵌入式压缩加密，进一步保护密集的 PolyProtect 模板，确保面部嵌入的不可逆性和不可关联性，有效地防止了从面部嵌入中泄露软生物识别属性而不损害识别准确性。

利用全同态加密增强面部分析的隐私

Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption

Although face recognition (FR) has achieved great success in recent years, it
is still challenging to accurately recognize faces in low-quality images due to
the obscured facial details. Nevertheless, it is often feasible to make
predictions about specific soft biometric (SB) attributes, such as gender, and
baldness even in dealing with low-quality images. In this paper, we propose a
novel multi-branch neural network that leverages SB attribute information to
boost the performance of FR. To this end, we propose a cross-attribute-guided
transformer fusion (CATF) module that effectively captures the long-range
dependencies and relationships between FR and SB feature representations. The
synergy created by the reciprocal flow of information in the dual
cross-attention operations of the proposed CATF module enhances the performance
of FR. Furthermore, we introduce a novel self-attention distillation framework
that effectively highlights crucial facial regions, such as landmarks by
aligning low-quality images with those of their high-quality counterparts in
the feature space. The proposed self-attention distillation regularizes our
network to learn a unified quality-invariant feature representation in
unconstrained environments. We conduct extensive experiments on various FR
benchmarks varying in quality. Experimental results demonstrate the superiority
of our FR method compared to state-of-the-art FR studies.

通过应用软生物特征属性和自我注意力蒸馏方法，我们提出了一种新颖的多分支神经网络，用于提高低质量图像下面部识别的性能，实验证明我们的方法优于最先进的研究成果。

CATFace: 基于跨属性引导变压器和自注意力蒸馏的低质量人脸识别

CATFace: Cross-Attribute-Guided Transformer with Self-Attention  Distillation for Low-Quality Face Recognition

The performance of a biometric system that relies on a single biometric
modality (e.g., fingerprints only) is often stymied by various factors such as
poor data quality or limited scalability. Multibiometric systems utilize the
principle of fusion to combine information from multiple sources in order to
improve recognition accuracy whilst addressing some of the limitations of
single-biometric systems. The past two decades have witnessed the development
of a large number of biometric fusion schemes. This paper presents an overview
of biometric fusion with specific focus on three questions: what to fuse, when
to fuse, and how to fuse. A comprehensive review of techniques incorporating
ancillary information in the biometric recognition pipeline is also presented.
In this regard, the following topics are discussed: (i) incorporating data
quality in the biometric recognition pipeline; (ii) combining soft biometric
attributes with primary biometric identifiers; (iii) utilizing contextual
information to improve biometric recognition accuracy; and (iv) performing
continuous authentication using ancillary information. In addition, the use of
information fusion principles for presentation attack detection and
multibiometric cryptosystems is also discussed. Finally, some of the research
challenges in biometric fusion are enumerated. The purpose of this article is
to provide readers a comprehensive overview of the role of information fusion
in biometrics.

本文综述了多生物特征融合技术，特别关注了三个问题：如何融合、何时融合以及融合什么，以及综合评述了利用辅助信息提高生物识别准确性的技术，并讨论了表示攻击检测和多生物识别加密系统的信息融合原理，最后列举了生物特征融合中的一些研究挑战。