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arXiv 2022年

作者： Huber, Marco Boutros, Fadi Luu, Anh Thi Raja, Kiran Ramachandra, Raghavendra Damer, Naser Neto, Pedro C. Gonçalves, Tiago Sequeira, Ana F. Cardoso, Jaime S. Tremoço, João Lourenço, Miguel Serra, Sergio Cermeño, Eduardo Ivanovska, Marija Batagelj, Borut Kronovšek, Andrej Peer, Peter Štruc, Vitomir Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Norwegian University of Science and Technology Norway INESC TEC Portugal University of Porto Portugal Yoonik Portugal Vaelsys R&D Spain University of Ljubljana Slovenia

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 International Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and attract solutions that deal with detecting face morphing attacks while protecting people’s privacy for ethical and legal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to outperforming the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://***/marcohuber/SYN-MAD-2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Competition

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SGLDBench: A Benchmark Suite for Stress-Guided Lightweight 3D Designs

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IEEE transactions on visualization and computer graphics 2025年 PP卷 PP页

作者： Junpeng Wang Dennis R Bukenberger Simon Niedermayr Christoph Neuhauser Jun Wu Rudiger Westermann Technische Universität München Computer Graphics & Visualization Group Garching Germany Delft University of Technology Department of Sustainable Design Engineering Delft Netherlands

We introduce the Stress-Guided Lightweight Design Benchmark (SGLDBench), a comprehensive benchmark suite for applying and evaluating material layout strategies to generate stiff, lightweight designs in 3D domains. SGLDBench provides a seamlessly integrated simulation and analysis framework, including six reference strategies and a scalable multigrid elasticity solver to efficiently execute these strategies and validate the stiffness of their results. This facilitates the systematic analysis and comparison of design strategies based on the mechanical properties they achieve. SGLDBench enables the evaluation of diverse load conditions and, through the tight integration of the solver, supports high-resolution designs and stiffness analysis. Additionally, SGLDBench emphasizes visual analysis to explore the relationship between the geometric structure of a design and the distribution of stresses, offering insights into the specific properties and behaviors of different design strategies. SGLDBench's specific features are highlighted through several experiments, comparing the results of reference strategies with respect to geometric and mechanical properties.

关键词： Shape optimization

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MCLA Task Offloading Framework for 5G-NR-V2X Based Heterogeneous VECNs

TechRxiv

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TechRxiv 2023年

作者： Mirza, Muhammad Ayzed Yu, Junsheng Raza, Salman Ahmed, Manzoor Asif, Muhammad Irshad, Azeem Kumar, Neeraj BUPT-QMUL EM Theory and Application International Research Lab Beijing University of Posts and Telecommunications Beijing100876 China School of Physics and Electronic Information Anhui Normal University Wuhu241003 China School of Intelligence and Digital Engineering Luoyang Vocational College of Science and Technology Luoyang471000 China Department of Computer Science National Textile University Faisalabad Pakistan School of Computer and Information Science Institute for AI Industrial Technology Research Hubei Engineering University Xiaogan432000 China Faculty of Computer Science Asghar Mall College Govt. of the Punjab Rawalpindi Pakistan Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Engineering Graphics Era University Uttarakhand Dehradun India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Department of Computer Science and Engineering Chandigarh University Gharuan Mohali India Department of Electrical and Computer Engineering Lebanese American University Beirut Lebanon Faculty of Computing and IT King Abdulaziz University Jeddah Saudi Arabia

Ensuring dependable quality of service (QoS) and quality of experience (QoE) for computation-intensive and delay-sensitive applications in vehicles can be a challenging task that impacts performance. While multi-access edge computing (MEC) based vehicular edge computing network (VECN) and vehicular cloudlets (VC) enable task offloading, but their prompt and optimal accessibility is another challenge. The conventional wireless technologies may not suffice to meet the stringent ultra-low latency and cost constraints of such applications. Nonetheless, the combination of different wireless technologies can enhance network performance and satisfy these requirements. Focusing on the computational efficacy of VECN, this paper proposes a mobility, contact, and computational load-aware (MCLA) task offloading scheme for heterogeneous VECN. The MCLA scheme dynamically considers the mobility, contact, and computational load of vehicles for making task offloading decisions. To optimize the performance, the MCLA scheme integrates the Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave communications. The MCLA scheme provides an opportunistic switching mechanism between these modes and heterogeneous radio access technologies (RATs) to reduce communication delays and costs. Moreover, the MCLA scheme leverages public vehicles (i.e., public buses), in proximity by using their computational power to manage computational latency and cost. Furthermore, it also considers the shareable computations from passengers’ mobile equipment within the public vehicle to improve the computation capacity of the public vehicles. Extensive evaluations and numerical results show that the proposed MCLA scheme significantly improves the task turnover ratio by 4%–15% with 4.7%–29.8% lower transmission and computation costs. © 2023, CC BY.

关键词： Delay-sensitive applications

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Sclera Segmentation and Joint Recognition Benchmarking Competition: SSRBC 2023

Sclera Segmentation and Joint Recognition Benchmarking Compe...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Abhijit Das Saurabh Atreya Aritra Mukherjee Matej Vitek Haiqing Li Caiyong Wang Guangzhe Zhao Fadi Boutros Patrick Siebke Jan Niklas Kolf Naser Damer Sun Ye Lu Hexin Fan Aobo You Sheng Sabari Nathan R. Suganya R. S. Rampriya Geetanjali Sharma P Priyanka Aditya Nigam Peter Peer Umapada Pal Vitomir Štruc Birla Institute of Technology and Science Pilani (BITS Pilani) India University of Ljubljana (UL Slovenia) Beijing University of Civil Engineering and Architecture (BUCEA) China Fraunhofer Institute for Computer Graphics Research IGD (IGD) Germany TU Darmstadt Germany Jilin University (JLU China) Cougar Inc (Japan) Vellore Institure of Technology Vellore (VIT India) Indian Institute of Technology Mandi (IIT-MD India) Indian Statistical Institute Kolkata (ISI India)

This paper presents the summary of the Sclera Segmentation and Joint Recognition Benchmarking Competition (SSRBC 2023) held in conjunction with IEEE International Joint Conference on Biometrics (IJCB 2023). Different from the previous editions of the competition, SSRBC 2023 not only explored the performance of the latest and most advanced sclera segmentation models, but also studied the impact of segmentation quality on recognition performance. Five groups took part in SSRBC 2023 and submitted a total of six segmentation models and one recognition technique for scoring. The submitted solutions included a wide variety of conceptually diverse deep-learning models and were rigorously tested on three publicly available datasets, i.e., MASD, SBVPI and MOBIUS. Most of the segmentation models achieved encouraging segmentation and recognition performance. Most importantly, we observed that better segmentation results always translate into better verification performance.

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UAV-based Digital Field Phenotyping for Crop Nitrogen Estimation using RGB Imagery

UAV-based Digital Field Phenotyping for Crop Nitrogen Estima...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Suchitra M Patil Sunita Choudhary Jana Kholová Krithika Anbazhagan Yogesh Parnandi Priyanka Gattu Srikanth Mallayee Prasad KSVV V Padma Kumar P Rajalakshmi Magesh Chandramouli J Adinarayana Centre of Studies in Resources Engineering Indian Institute of Technology Bombay Powai Mumbai Maharashtra India Crop Physiology and Modeling International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) Patancheru Telangana India Department of Information Technologies Czech University of Life Sciences Prague Prague Czech Republic Indian Institute of Technology Hyderabad Kandi Telangana India International Livestock Research Institute Patancheru Telangana India Computer Graphics Technology Purdue University NW Hammond IN USA

Nitrogen (N) is one of the essential nutrients required for healthy crop growth. Field phenotyping for nitrogen stress symptoms is laborious and time-consuming, that way, it is a major bottleneck in nutrition-inclusive agricultural research. Recent advancements in sensors and image processing facilitate color-based quantification of crop greenness from high-resolution RGB images. In this paper, we present unmanned aerial vehicle (UAV)-based digital field phenotyping for the estimation of crop nitrogen content. For this, we conducted a field experiment during the post-rainy season of 2021 at International Crops Research Institute for Semi-Arid Tropics (ICRISAT), Hyderabad, India with long-stature cereal model crop, sorghum (Sorghum bicolor L.) cultivated under three different regimes varying in moisture and soil nitrogen content. A high-resolution RGB sensor (XenmuseX5S) mounted on DJI Matric 210 quadcopter was used for capturing the spatiotemporal imagery. Five different RGB spectrum vegetation indices indicating crop greenness were correlated with ground truth values of crop N content using simple linear regression and stepwise backward regression. With a prediction potential of R 2 =0.65 and MAE=0.27 for an independent dataset, we present a stepwise backward linear regression model as a promising approach for real-time estimation of the N status of sorghum crop.

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SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

SynFacePAD 2023: Competition on Face Presentation Attack Det...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Meiling Fang Marco Huber Julian Fierrez Raghavendra Ramachandra Naser Damer Alhasan Alkhaddour Maksim Kasantcev Vasiliy Pryadchenko Ziyuan Yang Huijie Huangfu Yingyu Chen Yi Zhang Yuchen Pan Junjun Jiang Xianming Liu Xianyun Sun Caiyong Wang Xingyu Liu Zhaohua Chang Guangzhe Zhao Juan Tapia Lazaro Gonzalez-Soler Carlos Aravena Daniel Schulz Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Biometrics and Data Pattern Analytics Lab Universidad Autonoma de Madrid Spain Norwegian University of Science and Technology (NTNU) Norway ID R&D Inc New York US School of Cyber Science and Engineering Sichuan University Chengdu China School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture China Biometrics and Security Research Group Hochschule Darmstadt Darmstadt Germany I+D Vision Center Santiago Chile

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attracted a total of 8 participating teams with valid submissions from academia and industry. The competition aimed to motivate and attract solutions that target detecting face presentation attacks while considering synthetic-based training data motivated by privacy, legal and ethical concerns associated with personal data. To achieve that, the training data used by the participants was limited to synthetic data provided by the organizers. The submitted solutions presented innovations and novel approaches that led to outperforming the considered baseline in the investigated benchmarks.

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SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data

SYN-MAD 2022: Competition on Face Morphing Attack Detection ...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Marco Huber Fadi Boutros Anh Thi Luu Kiran Raja Raghavendra Ramachandra Naser Damer Pedro C. Neto Tiago Gonçalves Ana F. Sequeira Jaime S. Cardoso João Tremoço Miguel Lourenço Sergio Serra Eduardo Cermeño Marija Ivanovska Borut Batagelj Andrej Kronovšek Peter Peer Vitomir Štruc Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Norwegian University of Science and Technology Norway INESC TEC Portugal University of Porto Portugal Yoonik Portugal Vaelsys R&D Spain University of Ljubljana Slovenia

ISBN: (纸本)9781665463959

This paper presents a summary of the Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data (SYN-MAD) held at the 2022 In-ternational Joint Conference on Biometrics (IJCB 2022). The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 differ-ent countries. In the end, seven valid submissions were submitted by the participating teams and evaluated by the organizers. The competition was held to present and at-tract solutions that deal with detecting face morphing at-tacks while protecting people's privacy for ethical and le-gal reasons. To ensure this, the training data was limited to synthetic data provided by the organizers. The submitted solutions presented innovations that led to out-performing the considered baseline in many experimental settings. The evaluation benchmark is now available at: https://***/marcohuber/SYN-MAD-2022.

关键词： Industries Technological innovation Privacy Ethics Data privacy Biometrics (access control) Law

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QMagFace: Simple and Accurate Quality-Aware Face Recognition

arXiv

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arXiv 2021年

作者： Terhörst, Philipp Ihlefeld, Malte Huber, Marco Damer, Naser Kirchbuchner, Florian Raja, Kiran Kuijper, Arjan Norwegian University of Science and Technology Gjøvik Norway Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Technical University of Darmstadt Darmstadt Germany

In this work, we propose QMagFace, a simple and effective face recognition solution (QMagFace) that combines a quality-aware comparison score with a recognition model based on a magnitude-aware angular margin loss. The proposed approach includes model-specific face image qualities in the comparison process to enhance the recognition performance under unconstrained circumstances. Exploiting the linearity between the qualities and their comparison scores induced by the utilized loss, our quality-aware comparison function is simple and highly generalizable. The experiments conducted on several face recognition databases and benchmarks demonstrate that the introduced quality-awareness leads to consistent improvements in the recognition performance. Moreover, the proposed QMagFace approach performs especially well under challenging circumstances, such as cross-pose, cross-age, or cross-quality. Consequently, it leads to state-of-the-art performances on several face recognition benchmarks, such as 98.50% on AgeDB, 83.95% on XQLFQ, and 98.74% on CFP-FP. The code for QMagFace is publicly available1 © 2021, CC BY-NC-SA.

关键词： Face recognition

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Machine-learned regularization and polygonization of building segmentation masks

arXiv

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arXiv 2020年

作者： Zorzi, Stefano Bittner, Ksenia Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Austria Germany

We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks. Taking an image as input, we first predict building segmentation maps exploiting generic fully convolutional network (FCN). A generative adversarial network (GAN) is then involved to perform a regularization of building boundaries to make them more realistic, i.e., having more rectilinear outlines which construct right angles if required. This is achieved through the interplay between the discriminator which gives a probability of input image being true and generator that learns from discriminator’s response to create more realistic images. Finally, we train the backbone convolutional neural network (CNN) which is adapted to predict sparse outcomes corresponding to building corners out of regularized building segmentation results. Experiments on three building segmentation datasets demonstrate that the proposed method is not only capable of obtaining accurate results, but also of producing visually pleasing building outlines parameterized as polygons. Copyright © 2020, The Authors. All rights reserved.

关键词： Buildings

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Condition attributes, properties of decision rules, and discretisation: Analysis of relations and dependencies

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Procedia computer Science 2021年 192卷 3922-3931页

作者： Beata Zielosko Urszula Stańczyk University of Silesia in Katowice Będzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

When mining of input data is focused on rule induction, knowledge, discovered in exploration of existing patterns, is stored in combinations of certain conditions on attributes included in rule premises, leading to specific decisions. Through their properties, such as lengths, supports, cardinalities of rule sets, inferred rules characterise relations detected among variables. The paper presents research dedicated to analysis of these dependencies, considered in the context of various discretisation methods applied to the input data from stylometric domain. For induction of decision rules from data, Classical Rough Set Approach was employed. Next, based on rule properties, several factors were proposed and evaluated, reflecting characteristics of available condition attributes. They allowed to observe how variables and rule sets changed depending on applied discretisation algorithms.

关键词： Discretisation Decision rule Rule length Support Attribute Rough Sets

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