Publications Search Results
1 - 10 of 681
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PublicationAn optimization approach for a milling dynamics simulation based on Quantum Computing( 2024-02-01)
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PublicationDisentangling Morphed Identities for Face Morphing Detection( 2024)Morphing attacks keep threatening biometric systems, especially face recognition systems. Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown. At the same time, there is a constant concern regarding the lack of interpretability of deep learning models. Balancing performance and interpretability has been a difficult task for scientists. However, by leveraging domain information and proving some constraints, we have been able to develop IDistill, an interpretable method with state-of-the-art performance that provides information on both the identity separation on morph samples and their contribution to the final prediction. The domain information is learnt by an autoencoder and distilled to a classifier system in order to teach it to separate identity information. When compared to other methods in the literature it outperforms them in three out of five databases and is competitive in the remaining.
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PublicationMultispectral Imaging for Differential Face Morphing Attack Detection: A Preliminary Study( 2024)Face morphing attack detection is emerging as an increasingly challenging problem owing to advancements in high-quality and realistic morphing attack generation. Reliable detection of morphing attacks is essential because these attacks are targeted for border control applications. This paper presents a multispectral framework for differential morphing-attack detection (D-MAD). The D-MAD methods are based on using two facial images that are captured from the ePassport (also called the reference image) and the trusted device (for example, Automatic Border Control (ABC) gates) to detect whether the face image presented in ePassport is morphed. The proposed multi-spectral D-MAD framework introduce a multispectral image captured as a trusted capture to acquire seven different spectral bands to detect morphing attacks. Extensive experiments were conducted on the newly created Multispectral Morphed Datasets (MSMD) with 143 unique data subjects that were captured using both visible and multispectral cameras in multiple sessions. The results indicate the superior performance of the proposed multispectral framework compared to visible images.
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PublicationSFace2: Synthetic-Based Face Recognition with w-Space Identity-Driven Sampling( 2024)
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PublicationConstrained Optimal Experimental Design: Theory, Algorithm and Applications(Fraunhofer Verlag, 2024)While the literature on optimal experimental design (OED) is primarily concerned with unconstrained problems, it is also of interest to restrict individual experiments, certain design quantities, or even the entire experimental plan. A classic approach to OED models the problem as an unconstrained optimization problem over the infinite dimensional space of probability measures. This thesis introduces a general framework for constrained OED problems and discusses optimality criteria in form of saddle point conditions. Particular attention is paid to the required constraint qualifications. For the numerical solution of such constrained infinite-dimensional nonlinear OED problems this thesis proposes an adaptive discretization scheme, utilizing the derivative of the Lagrangian function. Furthermore, this thesis discusses the multi-criteria OED problem and presents algorithmic schemes for convex and non-convex objective functions. Finally, the algorithms are illustrated on an application example from chemical process engineering.
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PublicationBias and Diversity in Synthetic-based Face Recognition( 2024)
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PublicationAutomatic Segmentation and Scoring of 3D In Vitro Skin Models Using Deep Learning Methods( 2024)Cell-based in vitro skin models are an effective method for testing new medical compounds without any animal harming in the process. Histology serves as a cornerstone for evaluating in vitro models, providing critical insights into their structural integrity and functionality. The recently published BSGC score is a method to assess the quality of in vitro epidermal models, based on visual examination of histopathological images. However, this is very time-consuming and requires a high level of expertise. Therefore, this paper presents a method for automatic evaluation of three-dimensional in vitro epidermal models that involves segmentation and classification of epidermal layers in cross-sectional histopathological images. The input images are first pre-processed and in an initial classification step low-quality skin models are filtered. Subsequently, the individual epidermal strata are segmented and a masked image is generated for each stratum. The strata are scored individually using the masked images with a classification network per stratum. Finally the individual scores are merged into an overall weighted score per image. With an accuracy of 81% for the overall scoring the method provides promising results and allows for significant time savings and less subjectivity compared to the manual scoring process.
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PublicationTowards scalability for resource reconfiguration in robotic assembly line balancing problems using a modified genetic algorithm( 2024)Assembly lines are still one of the most used manufacturing systems in modern-day production. Most research affects the building of new lines and, less frequently, the reconfiguration of existing lines. However, the first is insufficient to meet the reconfigurable production paradigm required by volatile market demands. Consequent reconfiguration of resources by production requests affects companies’ competitiveness. This paper introduces a problem-specific genetic algorithm for optimizing the reconfiguration of a Robotic Assembly Line Balancing Problem with Task Types, including additional company constraints. First, we present the greenfield and brownfield optimization objectives, then a mathematical problem formulation and the composition of the genetic algorithm. We evaluate our model against an Integer Programming baseline on a reconfiguration dataset with multiple equipment alternatives. The results demonstrate the capabilities of the genetic algorithm for the greenfield case and showcase the possibilities in the brownfield case. With a scalability improvement through computation time decrease of up to ∼ 2.75 × , reduced number of equipment and workstations, but worse objective values, the genetic algorithm holds the potential for reconfiguring assembly lines. However, the genetic algorithm has to be further optimized for the reconfiguration to leverage its full potential.
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PublicationEfficient Explainable Face Verification based on Similarity Score Argument Backpropagation( 2024)
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PublicationAutomatische Generierung von Tabellendrillingen unter Beibehaltung originärer Metadaten( 2024)
