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IEEE International Joint Conference on Biometrics (IJCB)

作者： Harsh Rachalwar Meiling Fang Naser Damer Abhijit Das Birla Institute of Technology and Science Pilani (BITS Pilani) Secunderabad Telangana India Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Recently, morphing attack detection (MAD) solutions have achieved remarkable success with the aid of deep learning techniques. Despite the good performance achieved by binary label or binary pixel-wise supervised MAD models, the robustness of such models drops when facing variations in morphing attacks. In this work, we propose a novel process that leverages facial depth information to build a robust and generalized MAD. The depth map, representing the 3D shape of the face in a 2D image, is more informative compared to binary and binary pixel-wise map labels. To validate the idea we synthetically generated 3D depth map ground truth. Furthermore, we introduce a novel MAD architecture designed to capture subtle information from the 3D depth data. In addition, we analyze the training loss formulation to further enhance the MAD performance. Driven by the need for developing MAD solutions while preserving the privacy of individuals for legal and ethical reasons, we conduct our experiments on privacy-friendly synthetic training data and authentic evaluation data. The experimental results on existing public datasets in SYN-MAD 22 competition demonstrate the effectiveness of our proposed solution in terms of both robustness and generalization.

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Robust reconstruction of curved line structures in noisy point clouds

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Visual Informatics 2021年第3期5卷 1-14页

作者： Marcel Ritter Daniel Schiffner Matthias Harders Interactive Graphics and Simulation Group Department of Computer ScienceUniversity of InnsbruckAustria Airborne Hydromapping GmbH InnsbruckAustria DIPF|Leibniz Institute for Research and Information Education FrankfurtGermany

Point-based geometry representations have become widely used in numerous contexts,ranging from particle-based simulations,over stereo image matching,to depth sensing via light detection and *** application focus is on the reconstruction of curved line structures in noisy 3D point cloud *** algorithms operating on such point clouds often rely on the notion of a local *** the latter,our approach employs multi-scale neighborhoods,for which weighted covariance measures of local points are *** line structures are reconstructed via vector field tracing,using a bidirectional piecewise streamline *** also introduce an automatic selection of optimal starting points via multi-scale geometric *** pipeline development and choice of parameters was driven by an extensive,automated initial analysis process on over a million prototype test *** behavior of our approach is controlled by several parameters—the majority being set automatically,leaving only three to be controlled by a *** an extensive,automated final evaluation,we cover over one hundred thousand parameter sets,including 3D test geometries with varying curvature,sharp corners,intersections,data holes,and systematically applied varying types of ***,we analyzed different choices for the point of reference in the co-variance computation;using a weighted mean performed best in most *** addition,we compared our method to current,publicly available line reconstruction *** to thirty times faster execution times were achieved in some cases,at comparable error ***,we also demonstrate an exemplary application on four real-world 3D light detection and ranging datasets,extracting power line cables.

关键词： Computational geometry Noisy point clouds Line reconstruction Automatic Adaptive control

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Improving energy compaction of adaptive fourier decomposition 28

Improving energy compaction of adaptive fourier decompositio...

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28th European Signal Processing Conference, EUSIPCO 2020

作者： Borowicz, Adam Faculty of Computer Science Department of Digital Media and Computer Graphics Bialystok University of Technology Wiejska str. 45A Bialystok15-351 Poland

ISBN: (纸本)9789082797053

Adaptive Fourier decomposition (AFD) provides an expansion of an analytic function into a sum of basic signals, called mono-components. Unlike the Fourier series decomposition, the AFD is based on an adaptive rational orthogonal system, hence it is better suited for analyzing non-stationary data. The most popular algorithm for the AFD decomposes any signal in such a way that the energy of the low-frequency components is maximized. Unfortunately, this results in poor energy compaction of high-frequency components. In this paper, we develop a novel algorithm for the AFD. The key idea is to maximize the energy of any components no matter how big or small the corresponding frequencies are. A comparative evaluation was conducted of the signal reconstruction efficiency of the proposed approach and several conventional algorithms by using speech recordings. The experimental results show that with the new algorithm, it is possible to get a better performance in terms of the reconstruction quality and energy compaction property. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Signal reconstruction

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Immersive Situated Analysis of Dams' Behavior 5

Immersive Situated Analysis of Dams' Behavior

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5th International Conference on graphics and Interaction, ICGI 2023

作者： Trindade, Nuno Verdelho Leitao, Pedro Goncalves, Daniel Oliveira, Sergio Ferreira, Alfredo Instituto Superior Técnico University of Lisbon Graphics and Interaction Area INESC-ID Lisbon Portugal Instituto Superior Técnico University of Lisbon Computer Science and Engineering Department Lisbon Portugal Laboratório Nacional de Engenharia Civil Concrete Dams Department Lisbon Portugal

ISBN: (纸本)9798350358179

Dam safety control is a complex and challenging task that involves the analysis, monitoring, and behavior prediction of the various components of dams. Its primary objective is to ensure the safety and optimal performance of these structures. This undertaking involves the abstraction and interpretation of extensive structural, hydraulic, and geotechnical data, typically acquired through sensor networks embedded within the dam. This study introduces DamVR, a virtual reality tool for situated dam safety control data visualization. This tool allows dam engineers and other industry professionals to analyze the fluctuation of structural behavior over time. Its immersive environment contains a realistic representation of the dam structure, networks of sensors, and surroundings. It aims to frame the data within the visual context of the object of the analysis. This contextualization is intended to improve analytical reasoning and decision-making. The usability of DamVrwas evaluated by a group of domain experts and compared to an existing augmented reality tool in similar tasks. The results indicate that DamVris a usable and effective dam safety control data visualization tool. © 2023 IEEE.

关键词： Structural health monitoring

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First Competition on Presentation Attack Detection on ID Card

arXiv

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arXiv 2024年

作者： Tapia, Juan E. Damer, Naser Busch, Christoph Espin, Juan M. Barrachina, Javier Rocamora, Alvaro S. Ocvirk, Krištof Alessio, Leon Batagelj, Borut Patwardhan, Sushrut Ramachandra, Raghavendra Mudgalgundurao, Raghavendra Raja, Kiran Schulz, Daniel Aravena, Carlos da/sec-Biometrics and Internet Security Research Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Facephi company Spain Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Gjøvik Norway Santiago Chile

This paper summarises the Competition on Presentation Attack Detection on ID Cards (PAD-IDCard) held at the 2024 International Joint Conference on Biometrics (IJCB 2024). The competition attracted a total of ten registered teams, both from academia and industry. In the end, the participating teams submitted five valid submissions, with eight models to be evaluated by the organisers. The competition presented an independent assessment of current state-of-the-art algorithms. Today, no independent evaluation on cross-dataset is available;therefore, this work determined the state-of-the-art on ID cards. To reach this goal, a sequestered test set and baseline algorithms were used to evaluate and compare all the proposals. The sequestered test dataset contains ID cards from four different countries. In summary, a team that chose to be"Anonymous" reached the best average ranking results of 74.80%, followed very closely by the"IDVC" team with 77.65%. © 2024, CC BY-NC-ND.

关键词： Biometrics

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RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models

arXiv

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arXiv 2024年

作者： Kim, Jangyeong Kang, Donggoo Choi, Junyoung Wi, Jeonga Gwon, Junho Bae, Jiun Yoon, Dumim Han, Junghyun Graphics AI Lab. NCSOFT Korea Republic of Department of Computer Science and Engineering Korea University Korea Republic of Department of Image Chung-Ang University Korea Republic of

Text-to-texture generation has recently attracted increasing attention, but existing methods often suffer from the problems of view inconsistencies, apparent seams, and misalignment between textures and the underlying mesh. In this paper, we propose a robust text-to-texture method for generating consistent and seamless textures that are well aligned with the mesh. Our method leverages state-of-the-art 2D diffusion models, including SDXL and multiple ControlNets, to capture structural features and intricate details in the generated textures. The method also employs a symmetrical view synthesis strategy combined with regional prompts for enhancing view consistency. Additionally, it introduces novel texture blending and soft-inpainting techniques, which significantly reduce the seam regions. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods. © 2024, CC BY-NC-ND.

关键词： Mesh generation

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Enhancing Satellite Trail Detection in Night Sky Imagery with Automatic Salience Thresholding

Enhancing Satellite Trail Detection in Night Sky Imagery wit...

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Canadian Conference on computer and Robot Vision, CRV

作者： Nikolaus Kollo Yasushi Akiyama Jiju Peethambaran Department of Mathematics and Computer Science Graphics and Spatial Computing Lab Saint Mary's University Halifax Nova Scotia Canada

This paper proposes a novel automatic thresholding method called Automatic Salience Thresholding (AST) for creating binary masks for detecting and removing satellite streaks in night sky imagery. Our approach utilizes a combination of Gaussian filtering, a salience-based thresholding technique, morphological filtering and line detection using Probabilistic Hough Transformations to identify the satellite trail in the image. We evaluated our method on diverse datasets of night sky images containing satellite trails in varying lighting conditions. Our results show that AST outperforms the compared methods regarding accuracy and speed. Our proposed AST method provides a reliable and efficient solution for detecting satellite trails while balancing selectivity and sensitivity.

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PatchSwap: Boosting the Generalizability of Face Presentation Attack Detection by Identity-aware Patch Swapping

PatchSwap: Boosting the Generalizability of Face Presentatio...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Hamza Ali Kuijper Arjan Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Face presentation attack detection (PAD) is essential in mitigating spoofing attack vulnerabilities in face recognition systems. Despite the relatively good detection performance of PADs on known attacks, they tend to be challenged by unknown samples. To address this issue, we present our PatchSwap approach that aims at creating more challenging and complex bona fide, attack, and partial attack samples despite limited training resources. The PatchSwap operates by swapping intra-identity patches between training samples and correspondingly updates their pixel-wise mask label, all under a controlled strategy. The PatchSwap is deployed as an augmentation technique and can be effortlessly integrated into any model training process. The different choices towards our PatchSwap design are exhaustively investigated and proven in detailed studies. We conduct extensive experiments under intra-dataset and cross-dataset scenarios and on three different network backbones. The experimental results showed that the PatchSwap successfully induces significant gains in the PAD performance under different evaluation settings.

关键词： Training Solid modeling Protocols Image color analysis Face recognition Solids Boosting

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Semi-Supervised Language Models for Identification of Personal Health Experiential from Twitter Data: A Case for Medication Effects 20

Semi-Supervised Language Models for Identification of Person...

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20th Workshop on Biomedical Language Processing, BioNLP 2021

作者： Zhu, Minghao Jiang, Keyuan School of Computer Science and Technology Donghua University Shanghai 201620 China Department of Computer Information Technology and Graphics Purdue University Northwest HammondIN46323 United States

ISBN: (纸本)9781954085404

First-hand experience related to any changes of one’s health condition and understanding such experience can play an important role in advancing medical science and healthcare. Monitoring the safe use of medication drugs is an important task of pharmacovigilance, and first-hand experience of effects about consumers’ medication intake can be valuable to gain insight into how our human body reacts to medications. Social media have been considered as a possible alternative data source for gathering personal experience with medications posted by users. Identifying personal experience tweets is a challenging classification task, and efforts have been made to tackle the challenges using supervised approaches requiring annotated data. There exists an abundance of unlabeled Twitter data, and being able to use such data for training without suffering in classification performance is of great value, which can reduce the cost of laborious annotation process. We investigated two semi-supervised learning methods, with different mixes of labeled and unlabeled data in the training set, to understand the impact on classification performance. Our results from both pseudo-label and consistency regularization methods show that both methods generated a noticeable improvement in F1 score when the labeled set was small, and consistency regularization could still provide a small gain even a larger labeled set was used. © 2021 Association for Computational Linguistics

关键词： Social networking (online)

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