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Polyhedral Bunsen flames, induced by hydrodynamic and thermo-diffusive instabilities, are characterized by periodic trough and cusp cellular structures along the conical flame front. In this study, the effects of flow velocity, hydrogen content, and equivalence ratio on the internal cellular structure of premixed fuel-lean hydrogen/methane/air polyhedral flames are experimentally investigated. A high-spatial-resolution one-dimensional Raman/Rayleigh scattering system is employed to measure the internal scalar structures of polyhedral flames in troughs and cusps. Planar laser-induced fluorescence of hydroxyl radicals and chemiluminescence imaging measurements are used to quantify the flame front morphology. In the experiments, stationary polyhedral flames with varying flow velocities from 1.65 to 2.50 m/s, hydrogen contents from 50 to 83%, and equivalence ratios from 0.53 to 0.64 are selected and measured. The results indicate that the positively curved troughs exhibit significantly higher hydrogen mole fractions and local equivalence ratios compared to the negatively curved cusps, due to the respective focusing/defocusing effect of trough/cusp structure on highly diffusive hydrogen. The hydrogen mole fraction and local equivalence ratio differences between troughs and cusps are first increased and then decreased with increasing measurement height from 5 to 13 mm, due to the three-dimensional effect of the flame front. With increasing flow velocity from 1.65 to 2.50 m/s, the hydrogen mole fraction and local equivalence ratio differences between troughs and cusps decrease, which is attributed to the overall decreasing curvatures in troughs and cusps due to the decreased residence time and increased velocity-induced strain. With increasing hydrogen content from 50 to 83%, the hydrogen mole fraction and local equivalence ratio differences between troughs and cusps are amplified, due to the enhanced effects of the flame front curvature and the differential diffusion of hydrogen. With increasing equivalence ratio from 0.53 to 0.64, a clear increasing trend in hydrogen mole fraction and equivalence ratio differences between troughs and cusps is observed at constant flow velocity condition, which is a trade-off result between increasing effective Lewis number and increasing curvatures in troughs and cusps.
Mobile Contactless Fingerprint Presentation Attack Detection: Generalizability and Explainability
(2024)
Contactless fingerprint recognition is an emerging biometric technology that has several advantages over contact-based schemes, such as improved user acceptance and fewer hygienic concerns. Like for most other biometrics, Presentation Attack Detection (PAD) is crucial to preserving the trustworthiness of contactless fingerprint recognition methods. For many contactless biometric characteristics, Convolutional Neural Networks (CNNs) represent the state-of-the-art of PAD algorithms. For CNNs, the ability to accurately classify samples that are not included in the training is of particular interest, since these generalization capabilities indicate robustness in real-world scenarios. In this work, we focus on the generalizability and explainability aspects of CNN-based contactless fingerprint PAD methods. Based on previously obtained findings, we selected four CNN-based methods for contactless fingerprint PAD: two PAD methods designed for other biometric characteristics, an algorithm for contact-based fingerprint PAD and a general-purpose ResNet18. For our evaluation, we use four databases and partition them using Leave-One-Out (LOO) protocols. Furthermore, the generalization capability to a newly captured database is tested. Moreover, we explore t-SNE plots as a means of explainability to interpret our results in more detail. The low D-EERs obtained from the LOO experiments (below 0.1% D-EER for every LOO group) indicate that the selected algorithms are well-suited for the particular application. However, with an D-EER of 4.14%, the generalization experiment still has room for improvement.
Zeit für Veränderung
(2024)
Der Beitrag nimmt unter Bezugnahme auf die Beiträge des Schwerpunkts „Staatliche Anerkennung“ einen Ausblick zur Perspektive der staatlichen Anerkennung vor und formuliert drei Impulse, die zu einer Neujustierung rund um Fragen der Qualifizierung von Sozialarbeiter_innen und Sozialpädagog_innen beitragen könnten.
Die staatliche Anerkennung von Sozialarbeiter_innen und Sozialpädagog_innen blickt auf eine lange Tradition zurück und ist seit vielen Jahren Gegenstand zahlreicher und kontroverser Auseinandersetzungen. Der Beitrag widmet sich neben einer grundlegenden Einführung zur Thematik, der gegenwärtigen Relevanz sowie der aktuellen Vergabepraxis der staatlichen Anerkennung, identifiziert zentrale
Diskussionsbedarfe und führt in den Schwerpunkt ein.
Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible.
We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification.
Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.
Face Morphing Attacks pose a threat to the security of identity documents, especially with respect to a subsequent access control process, because they allow both involved individuals to use the same document. Several algorithms are currently being developed to detect Morphing Attacks, often requiring large data sets of morphed face images for training. In the present study, face embeddings are used for two different purposes: first, to pre-select images for the subsequent large-scale generation of Morphing Attacks, and second, to detect potential Morphing Attacks. Previous studies have demonstrated the power of embeddings in both use cases. However, we aim to build on these studies by adding the more powerful MagFace model to both use cases, and by performing comprehensive analyses of the role of embeddings in pre-selection and attack detection in terms of the vulnerability of face recognition systems and attack detection algorithms. In particular, we use recent developments to assess the attack potential, but also investigate the influence of morphing algorithms. For the first objective, an algorithm is developed that pairs individuals based on the similarity of their face embeddings. Different state-of-the-art face recognition systems are used to extract embeddings in order to pre-select the face images and different morphing algorithms are used to fuse the face images. The attack potential of the differently generated morphed face images will be quantified to compare the usability of the embeddings for automatically generating a large number of successful Morphing Attacks. For the second objective, we compare the performance of the embeddings of two state-of-the-art face recognition systems with respect to their ability to detect morphed face images. Our results demonstrate that ArcFace and MagFace provide valuable face embeddings for image pre-selection. Various open-source and commercial-off-the-shelf face recognition systems are vulnerable to the generated Morphing Attacks, and their vulnerability increases when image pre-selection is based on embeddings compared to random pairing. In particular, landmark-based closed-source morphing algorithms generate attacks that pose a high risk to any tested face recognition system. Remarkably, more accurate face recognition systems show a higher vulnerability to Morphing Attacks. Among the systems tested, commercial-off-the-shelf systems were the most vulnerable to Morphing Attacks. In addition, MagFace embeddings stand out as a robust alternative for detecting morphed face images compared to the previously used ArcFace embeddings. The results endorse the benefits of face embeddings for more effective image pre-selection for face morphing and for more accurate detection of morphed face images, as demonstrated by extensive analysis of various designed attacks. The MagFace model is a powerful alternative to the often-used ArcFace model in detecting attacks and can increase performance depending on the use case. It also highlights the usability of embeddings to generate large-scale morphed face databases for various purposes, such as training Morphing Attack Detection algorithms as a countermeasure against attacks.
This study investigates the diffusion of AI-based service applications within the business models of German manufacturing industries, surveying 162 decision-makers. The integration of AI into business model is assessed through the Business Model Canvas (BMC) framework, evaluating its value in terms of effectiveness as well as efficiency. Rather than focusing on specific use cases, the study delves into the intended usage of value-driven AI services references to enhance effectiveness and efficiency across various elements of the business models. Through this research, eleven service values have been identified. Each service vale corresponds to a distinct element of the BMC. Decision-makers were surveyed using a Confirmation/Disconfirmation (C/D) paradigm to measure the disparities between their current and target performance levels. Consequently, this study provides valuable insights from the perspective of decision makers regarding the current and desired state of AI integration in the German manufacturing industry, taking into account AI usage or no AI usage at the time of data collection.
In many forensic scenarios, criminals often attempt to conceal their identity by covering their face and other distinctive body parts. In such situations, physical evidence may, however, reveal other unique characteristics, e.g. hands, which can be used to identify offenders. In this context, several state-of-the-art biometric recognition systems have been proposed recently. These recognition systems offer high identification performance in restricted environments. However, in forensic scenarios, the environment is often unconstrained, making biometric identification considerably more difficult, with a consequent decrease in accuracy. In this article, we explore methods (e.g. hand alignment and information fusion) to improve the identification of subjects within forensic investigations. Experimental results show that explored techniques play an important role in the improvement of the identification performance of existing schemes: the combination of hand alignment and information fusion results in the highest Rank-1 identification performance improvement of up to 13.10% (i.e., 26.30% vs. 13.20%) and 16.30% (i.e., 77.00% vs. 60.70%) with respect to the baseline for the unconstrained databases NTU-PI_v1 and HaGRID, respectively ( https://github.com/ljsoler/IF-HA-HandRecognition ).
The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multi-biometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes seem inadequate for indexing (workload reduction) in biometric identification systems. More specifically, they have been used in identification systems that perform exhaustive searches, leading to a degradation of computational efficiency. To overcome these limitations, we present an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates and is agnostic with respect to biometric characteristics and biometric template protection schemes. To this end, a multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics. Experimental results reported on publicly available databases using state-of-the-art Deep Neural Network (DNN)-based embedding extractors show that the protected multi-biometric identification system can reduce the computational workload to approximately 57% (indexing up to three types of biometric characteristics) and 53% (indexing up to two types of biometric characteristics), while simultaneously improving the biometric performance of the baseline biometric system at the high-security thresholds. Code is available at https://github.com/dosorior/FBP-Multi-biometric-Indexing.
Millistructured Coiled Flow Inverter for Biphasic Continuous Flow 5‐Chloromethylfurfural Synthesis
(2024)
Syntheses of alternative platform chemicals, such as 5‐chloromethylfurfural (CMF), from bio‐based starting materials are often associated with complicated kinetic schemes and mass transfer processes. Millistructured flow reactor concepts can help to elucidate kinetic schemes and determine rate constants which are of crucial importance for the design of respective technical processes. For the first time, the influence of proton concentration on the rate constants involved in the biphasic synthesis of CMF is systematically investigated. Results are discussed in terms of green chemistry metrics.