Facilitating free travel in the Schengen area - A position paper by the European Association for Biometrics

Due to migration, terror‐threats and the viral pandemic, various EU member states have re‐established internal border control or even closed their borders. European Association for Biometrics (EAB), a non‐profit organisation, solicited the views of its members on ways which biometric technologies and services may be used to help with re‐establishing open borders within the Schengen area while at the same time mitigating any adverse effects. From the responses received, this position paper was composed to identify ideas to re‐establish free travel between the member states in the Schengen area. The paper covers the contending needs for security, open borders and fundamental rights as well as legal constraints that any technological solution must consider. A range of specific technologies for direct biometric recognition alongside complementary measures are outlined. The interrelated issues of ethical and societal considerations are also highlighted. Provided a holistic approach is adopted, it may be possible to reach a more optimal trade‐off with regards to open borders while maintaining a high‐level of security and protection of fundamental rights. European Association for Biometrics and its members can play an important role in fostering a shared understanding of security and mobility challenges and their solutions

Efficient privacy-preserving biometric identification in largescale multibiometric systems

In recent years, applications of biometric systems on national and international scale have appeared. Biometric identification is one of the important operational modes of such systems. It entails ascertaining the data subject identity corresponding to a given biometric sample, solely using the information from said biometric sample, i.e. effectively conducting a nearestneighbour search. The na¨ıve search method, i.e. an exhaustive (linear) search of the biometric enrolment database, suffers from two drawbacks, namely: high computational workload and increased probability of false positive occurrences. Consequently, research into computationally efficient methods of biometricidentification is necessary; it is the main topic covered in this thesis. Specifically, the key contributions of this thesis are: • Formulation of a taxonomy for conceptual categorisation of methods of efficient biometric identification. A comprehensive survey of the relevant existing publications and organisation thereof in the context of the developed taxonomy. • Development of methods which substantially decrease (by space search and/or template comparison cost reduction) the computational workload requirements of the biometric identification transactions, including: – Methods which take advantage of the intrinsic properties of certain types of biometric characteristics and/or biometric feature representations. – Methods which can be applied irrespective of the type of biometric characteristic and the biometric feature representation. – Methods which utilise biometric information fusion. • Development of methods (both general purpose and biometric characteristic specific) of biometric template protection in the aforementioned context of computationally efficient biometric identification systems. • Development of methods relevant to other (e.g. stress testing and usability) aspects of the operational biometric identification systems

Summer Student Report - Project Kryolize

The purpose of this document is to describe the work and results obtained by the author during his summer student internship at CERN. The author of this document was attached to the project Kryolize as a software developer, overtaking the job from a recently departed technical student

Turning a vulnerability into an asset: Accelerating Facial Identification with Morphing

In recent years, morphing of facial images has arisen as an important attack vector on biometric systems. Detection of morphed images has proven challenging for automated systems and human experts alike. Likewise, in recent years, the importance of efficient (fast) biometric identification has been emphasised by the rapid rise and growth of large-scale biometric systems around the world. In this paper, the aforementioned, hitherto unrelated, topics within the biometrics domain are combined: the properties of morphed images are exploited for the purpose of improving the transaction times of a biometric identification system. Specifically, morphs of two or more samples are used in the pre-selection step of a two-stage biometric identification system. In a proof-of-concept experimental evaluation using two state-of-the-art open-source facial recognition frameworks it is shown, that the proposed system achieves hit rates comparable to that of an exhaustive search-based baseline, while significantly reducing the penetration rate (and thus the computational workload) associated with the biometric identification transactions

Computational Workload in Biometric Identification Systems: An Overview

Computational workload is one of the key challenges in biometric identification systems. The na{\"i}ve 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, a 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

Reliable detection of doppelgängers based on deep face representations

Abstract Doppelgängers (or lookalikes) usually yield an increased probability of false matches in a facial recognition system, as opposed to random face image pairs selected for non‐mated comparison trials. In this work, the impact of doppelgängers on the HDA Doppelgänger and Disguised Faces in The Wild databases is assessed using a state‐of‐the‐art face recognition system. It is found that doppelgänger image pairs yield very high similarity scores resulting in a significant increase of false match rates. Further, a doppelgänger detection method is proposed, which distinguishes doppelgängers from mated comparison trials by analysing differences in deep representations obtained from face image pairs. The proposed detection system employs a machine learning‐based classifier, which is trained with generated doppelgänger image pairs utilising face morphing techniques. Experimental evaluations conducted on the HDA Doppelgänger and Look‐Alike Face databases reveal a detection equal error rate of approximately 2.7% for the task of separating mated authentication attempts from doppelgängers