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。

生物特征融合的综合概述

A Comprehensive Overview of Biometric Fusion

Nowadays, the adoption of face recognition for biometric authentication
systems is usual, mainly because this is one of the most accessible biometric
modalities. Techniques that rely on trespassing these kind of systems by using
a forged biometric sample, such as a printed paper or a recorded video of a
genuine access, are known as presentation attacks, but may be also referred in
the literature as face spoofing. Presentation attack detection is a crucial
step for preventing this kind of unauthorized accesses into restricted areas
and/or devices. In this paper, we propose a novel approach which relies in a
combination between intrinsic image properties and deep neural networks to
detect presentation attack attempts. Our method explores depth, salience and
illumination maps, associated with a pre-trained Convolutional Neural Network
in order to produce robust and discriminant features. Each one of these
properties are individually classified and, in the end of the process, they are
combined by a meta learning classifier, which achieves outstanding results on
the most popular datasets for PAD. Results show that proposed method is able to
overpass state-of-the-art results in an inter-dataset protocol, which is
defined as the most challenging in the literature.

本论文提出了一种基于深度神经网络和元学习分类器相结合的方法，该方法利用图像固有属性探测面部 “假冒攻击”，在 PAD 领域最具挑战性的跨数据集协议上取得了超越最新成果的显著结果。