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

Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition

Systems that analyse faces have seen significant improvements in recent years and are today used in numerous application scenarios. However, these systems have been found to be negatively affected by facial alterations such as tattoos. To better understand and mitigate the effect of facial tattoos in facial analysis systems, large datasets of images of individuals with and without tattoos are needed. To this end, we propose a generator for automatically adding realistic tattoos to facial images. Moreover, we demonstrate the feasibility of the generation by using a deep learning-based model for removing tattoos from face images. The experimental results show that it is possible to remove facial tattoos from real images without degrading the quality of the image. Additionally, we show that it is possible to improve face recognition accuracy by using the proposed deep learning-based tattoo removal before extracting and comparing facial features