Automated fingerprint recognition systems, while widely used, are still vulnerable to presentation attacks (PAs). The attacks can employ a wide range of presentation attack species (i.e., artifacts), varying from low-cost artifacts to sophisticated materials. A number of presentation attack detection (PAD) approaches have been specifically designed to detect and counteract presentation attacks on fingerprint systems. In this chapter, we study and analyze the well-employed Convolutional Neural Networks (CNN) with different architectures for fingerprint PAD by providing an extensive analysis of 23 different architectures in CNNs. In addition, this chapter presents a new approach introducing vision transformers for fingerprint PAD and validates it on two different public datasets, LivDet2015 and LivDet2019, used for fingerprint PAD. With the analysis of vision transformer-based F-PAD, this chapter covers both spectrum of CNNs and vision transformers to provide the reader with a one-place reference for understanding the performance of various architectures. Vision transformers provide at par results for the fingerprint PAD compared to CNNs with more extensive training duration suggesting its promising nature. In addition, the chapter presents the results for a partial open-set protocol and a true open-set protocol analysis where neither the capture sensor nor the material in the testing set is known at the training phase. With the true open-set protocol analysis, this chapter presents the weakness of both CNN architectures and vision transformers in scaling up to unknown test data, i.e., generalizability challenges.

Biometric systems have experienced a large development over the last years since they are accurate, secure and in many cases, more user convenient than traditional credential-based access control systems. In spite of their benefits, biometric systems are vulnerable to attack presentations, which can be easily carried out by a non-authorised subject without having a deep computational knowledge. This way, he/she can gain access to several applications where biometric systems are frequently deployed, such as bank accounts and smartphone unlocking. In order to mitigate such threats, we present in this work a study on the feasibility of using the Fisher Vector (FV) representation to spot unknown-attack presentations over different biometric modalities such as fingerprint, face and voice. By learning a common feature space from a set of local features, extracted from known samples, the FVs lead to the construction of reliable discriminative models which can successfully distinguish a bona fide presentation from an attack presentation. The experimental evaluation over publicly available databases (i.e. LivDets, CASIA-FASD, SiW-M and ASVspoof, among others) yields error rates outperforming most state-of-the-art algorithms for challenging scenarios where species, recipies or capture devices remain unknown.

Iris recognition technology has attracted an increasing interest in the last decades in which we have witnessed a migration from research laboratories to real-world applications. The deployment of this technology raises questions about the main vulnerabilities and security threats related to these systems. Among these threats, presentation attacks stand out as some of the most relevant and studied. Presentation attacks can be defined as the presentation of human characteristics or artifacts directly to the capture device of a biometric system trying to interfere with its normal operation. In the case of the iris, these attacks include the use of real irises as well as artifacts with different levels of sophistication such as photographs or videos. This chapter introduces iris Presentation Attack Detection (PAD) methods that have been developed to reduce the risk posed by presentation attacks. First, we summarize the most popular types of attacks including the main challenges to address. Second, we present a taxonomy of PAD methods as a brief introduction to this very active research area. Finally, we discuss the integration of these methods into iris recognition systems according to the most important scenarios of practical application.

The world-wide plastics pollution into nature enters the human food chain as degradation products, influencing environmental habitat and health. Micro- and nanoplastics as foreign bodies contaminating beverages and foods on the cellular level are likely to have a long-term impact on nutrition growth, public and personal health. Nanoparticles and their spatial location are generally difficult to detect in biological specimens, especially plastic particles due to their analogous hydrocarbon-based matrix. Reliable analysis methods for the discovery of micro- and nanoplastic particles in entities are not fully established yet. We are therefore developing a correlative workflow for detecting plastics residuals in cellular tissue. We determined that light microscopy (LM) is usable for a coarse estimation of the contamination grade of plant samples. Due to its optical diffraction limit, LM rather detects nanoparticle patches than single particles, shown with fluorescent particles. Scanning electron microscopy (SEM) instead provides higher resolution, especially for single particle detection. EM sample staining is still required to depict polymerous nanoparticle specimens. Using LM and SEM data in a CLEM approach with TESCAN CORAL, discovery of contaminated plant areas is possible with single nanoparticle resolution. It reveals that nanoparticles seem to be tightly attached to the plant material. It indicates further that basic food cleaning procedures might be insufficient for particle removal. The workflow circumvents correlative sample 3D-topography issues. But insights into the plant matrix is best possible with z-information. Therefore, we additionally use focused ion beam (FIB) to determine inclusions of micro- and nanoplastics in the tissue/cellular matrix, investigating the optimal procedural approach as model for biomaterial systems. Procedural correlative microscopy provides a promising analysis method for the semiquantitative analysis of micro- and nanoparticles plant contaminations.

A variety of evaluation methods for user interfaces (UI) exist such as usability testing,

cognitive walkthrough, and heuristic evaluation. However, UIs such as guidance systems at transit

hubs must be evaluated in their intended application field to allow the effective and valid identi-

fication of usability flaws. However, what if evaluations are not feasible in real environments, or

laboratorial conditions cannot be ensured? Based on adapted heuristics, in the present study, the

method of heuristic evaluation is combined with immersive Virtual Reality (VR) for the identification

of usability flaws of dynamic guidance systems (DGS) at transit hubs. The study involved usability

evaluations of nine DGS concepts using the newly proposed method. The results show that compared

to computer-based heuristic evaluations, the use of immersive VR led to the identification of an

increased amount of “severe” usability flaws as well as overall usability flaws. Within a qualitative

assessment, immersive VR is validated as a suitable tool for conducting heuristic evaluations in-

volving significant advantages such as the creation of realistic experiences in laboratorial conditions.

Future work seeks to further prove the suitability of using immersive VR for heuristic evaluations

and compare the proposed method to other evaluative methods.

Deep Learning has enabled many advances in machine learning applications in the last few years. However, since current Deep Learning algorithms require much energy for computations, there are growing concerns about the associated environmental costs. Energy-efficient Deep Learning has received much attention from researchers and has already made much progress in the last couple of years. This paper aims to gather information about these advances from the literature and show how and at which points along the lifecycle of Deep Learning (IT-Infrastructure, Data, Modeling, Training, Deployment, Evaluation) it is possible to reduce energy consumption.

In the past decade, globalization and digitization have not only changed the way we work, but also the environment in which we work. More and more companies are introducing desk sharing office concepts in which employees must share a workstation. However, this poses challenges for ergonomic workplace design as constant and ergonomically correct workstation settings can hardly be guaranteed. Neglecting ergonomics at workplace, though, can cause musculoskeletal disorders. Therefore, a concept and prototype for a system are proposed which automatically adjusts the workstation to the individual's anthropometric characteristics. A setup of different mechanical and electronical components using microcontrollers, ultrasonic distance sensors and linear actuators assures an automatic adjustment where users only must sign in with their ID. An initial field study shows that the system can achieve high user acceptance. Simplicity, speed, and convenience are seen as added value of the system. The results have potential for future studies.