Systems incorporating biometric technologies have become ubiquitous in personal, commercial, and governmental identity management applications. Both cooperative (e.g., access control) and noncooperative (e.g., surveillance and forensics) systems have benefited from biometrics. Such systems rely on the uniqueness of certain biological or behavioral characteristics of human beings, which enable for individuals to be reliably recognized using automated algorithms. Recently, however, there has been a wave of public and academic concerns regarding the existence of systemic bias in automated decision systems (including biometrics). Most prominently, face recognition algorithms have often been labeled as “racist” or “biased” by the media, nongovernmental organizations, and researchers alike. The main contributions of this article are: 1) an overview of the topic of algorithmic bias in the context of biometrics; 2) a comprehensive survey of the existing literature on biometric bias estimation and mitigation; 3) a discussion of the pertinent technical and social matters; and 4) an outline of the remaining challenges and future work items, both from technological and social points of view.

Since early 2020, the COVID-19 pandemic has had a considerable impact on many aspects of daily life. A range of different measures have been implemented worldwide to reduce the rate of new infections and to manage the pressure on national health services. A primary strategy has been to reduce gatherings and the potential for transmission through the prioritisation of remote working and education. Enhanced hand hygiene and the use of facial masks have decreased the spread of pathogens when gatherings are unavoidable. These particular measures present challenges for reliable biometric recognition, e.g. for facial-, voice-and hand-based biometrics. At the same time, new challenges create new opportunities and research directions, e.g. renewed interest in non-constrained iris or periocular recognition, touch-less fingerprint-and vein-based authentication and the use of biometric characteristics for disease detection. This article presents an overview of the research carried out to address those challenges and emerging opportunities.

The development of large-scale facial identification systems that provide privacy protection of the enrolled subjects represents an open challenge. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes appear to be unsuitable for indexing (workload reduction) in biometric identification systems. More precisely, they have been utilised in identification systems performing exhaustive searches, thereby leading to degradations of the computational efficiency. In this work, we propose a privacy-preserving face identification system which utilises a Product Quantisation-based hash look-up table for indexing and retrieval of protected face templates. These face templates are protected through fully homomorphic encryption schemes, thereby guaranteeing high privacy protection of the enrolled subjects. For the best configuration, the experimental evaluation carried out over closed-set and open-set settings shows the feasibility of the proposed technique for the use in large-scale facial identification systems: a workload reduction down to 0.1% of a baseline approach performing an exhaustive search is achieved together with a low pre-selection error rate of less than 1%. In terms of biometric performance, a False Negative Identification Rate (FNIR) in range of 0.0% -0.2% is obtained for practical False Positive Identification Rate (FPIR) values on the FEI and FERET face databases. In addition, our proposal shows competitive performance on unconstrained databases, e.g., the LFW face database. To the best of the authors’ knowledge, this is the first work presenting a competitive privacy-preserving workload reduction scheme which performs template comparisons in the encrypted domain.

The computational workload is one of the key challenges in biometric identification systems. The naïve retrieval method based on an exhaustive search becomes impractical with the growth of the number of the enrolled data subjects. Consequently, in recent years, many methods with the aim of reducing or optimising the computational workload, and thereby speeding‐up the identification transactions, in biometric identification systems have been developed. In this article, taxonomy for conceptual categorisation of such methods is presented, followed by a comprehensive survey of the relevant academic publications, including computational workload reduction and software/hardware‐based acceleration. Lastly, the pertinent technical considerations and trade‐offs of the surveyed methods are discussed, along with an industry perspective, and open issues/challenges in the field.

Computationally efficient, accurate, and privacy-preserving data storage and retrieval are among the key challenges faced by practical deployments of biometric identification systems worldwide. In this work, a method of protected indexing of biometric data is presented. By utilising feature-level fusion of intelligently paired templates, a multi-stage search structure is created. During retrieval, the list of potential candidate identities is successively pre-filtered, thereby reducing the number of template comparisons necessary for a biometric identification transaction. Protection of the biometric probe templates, as well as the stored reference templates and the created index is carried out using homomorphic encryption. The proposed method is extensively evaluated in closed-set and open-set identification scenarios on publicly available databases using two state-of-the-art open-source face recognition systems. With respect to a typical baseline algorithm utilising an exhaustive search-based retrieval algorithm, the proposed method enables a reduction of the computational workload associated with a biometric identification transaction by 90%, while simultaneously suffering no degradation of the biometric performance. Furthermore, by facilitating a seamless integration of template protection with open-source homomorphic encryption libraries, the proposed method guarantees unlinkability, irreversibility, and renewability of the protected biometric data.

The application of facial cosmetics may cause substantial alterations in the facial appearance, which can degrade the performance of facial biometrics systems. Additionally, it was recently demonstrated that makeup can be abused to launch so-called makeup presentation attacks. More precisely, an attacker might apply heavy makeup to obtain the facial appearance of a target subject with the aim of impersonation or to conceal their own identity. We provide a comprehensive survey of works related to the topic of makeup presentation attack detection, along with a critical discussion. Subsequently, we assess the vulnerability of a commercial off-the-shelf and an open-source face recognition system against makeup presentation attacks. Specifically, we focus on makeup presentation attacks with the aim of impersonation employing the publicly available Makeup Induced Face Spoofing (MIFS) and Disguised Faces in the Wild (DFW) databases. It is shown that makeup presentation attacks might seriously impact the security of face recognition systems. Further, we propose different image pair-based, i.e. differential, attack detection schemes which analyse differences in feature representations obtained from potential makeup presentation attacks and corresponding target face images. The proposed detection systems employ various types of feature extractors including texture descriptors, facial landmarks, and deep (face) representations. To distinguish makeup presentation attacks from genuine, i.e. bona fide presentations, machine learning-based classifiers are used. The classifiers are trained with a large number of synthetically generated makeup presentation attacks utilising a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database and a subset of the DFW database reveal that deep face representations achieve competitive detection equal error rates of 0.7% and 1.8%, respectively.