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Kvc-Ongoing: Keystroke Verification Challenge

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SSRN

SSRN 2024年

作者： Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami DeAndres-Tame, Ivan Damer, Naser Fierrez, Julian Garcia, Javier-Ortega Acien, Alejandro Gonzalez, Nahuel Shadrikov, Andrei Gordin, Dmitrii Schmitt, Leon Wimmer, Daniel Großmann, Christoph Krieger, Joerdis Heinz, Florian Krestel, Ron Mayer, Christoffer Haberl, Simon Gschrey, Helena Yamagishi, Yosuke Saha, Sanjay Rasnayaka, Sanka Wickramanayake, Sandareka Sim, Terence Gutfeter, Weronika Baran, Adam Krzyszton, Mateusz Jaskóla, Przemys law Lab Universidad Autonoma de Madrid Spain Fraunhofer Institute for Computer Graphics Research IGD Germany Department of Computer Science Technical University of Darmstadt Germany Universidad de Buenos Aires Argentina Verigram LLC Singapore Citix Kazakhstan Regensburg University Of Applied Sciences Germany Division of Radiology and Biomedical Engineering The University of Tokyo Japan National University of Singapore Singapore University of Moratuwa Sri Lanka NASK – National Research Institute Poland

This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing), on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards simulating real-life conditions. The results on the evaluation set of KVC-onGoing have proved the high discriminative power of keystroke dynamics, reaching values as low as 3.33% of Equal Error Rate (EER) and 11.96% of False Non-Match Rate (FNMR) @1% False Match Rate (FMR) in the desktop scenario, and 3.61% of EER and 17.44% of FNMR @1% at FMR in the mobile scenario, significantly improving previous state-of-the-art results. Concerning demographic fairness, the analyzed scores reflect the subjects’ age and gender to various extents, not negligible in a few cases. The framework runs on Codalab. © 2024, The Authors. All rights reserved.

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Vehicular Perception Based on Inertial Sensing: A Structured Mapping of Approaches and Methods

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SN computer science 2020年第5期1卷 1-24页

作者： Menegazzo, Jeferson von Wangenheim, Aldo Graduate Program in Computer Science (PPGCC) Image Processing and Computer Graphics Lab (LAPIX) Department of Informatics and Statistics (INE) Brazilian Institute for Digital Convergence (INCoD) Federal University of Santa Catarina (UFSC) Santa Catarina Florianópolis Brazil

In this paper, we present a structured literature mapping of the state-of-the-art of vehicular perception methods and approaches using inertial sensors. An in-depth investigation and classification were performed employing the results of a systematic literature review. The analysis focused on identifying methods that capture signals provided by inertial sensors such as accelerometers and gyroscopes to recognize transient or persistent events associated with the vehicle’s movement. We classified these events into vehicular exteroception, associated with potholes, cracks, speed bumps, pavement type, conservation state;and vehicular proprioception, associated with lane change, braking, skidding, aquaplaning, turning right or left. Through the comprehensive study of publications in a 7-year time window, in addition to the methods, we have also identified their dependency factors, hardware platforms and applications. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Driver behaviour Driving style Inertial sensors Intelligent transport systems Road conditions Road surface anomaly

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

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

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

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arXiv 2023年

作者： Fang, Meiling Huber, Marco Fierrez, Julian Ramachandra, Raghavendra Damer, Naser Alkhaddour, Alhasan Kasantcev, Maksim Pryadchenko, Vasiliy Yang, Ziyuan Huangfu, Huijie Chen, Yingyu Zhang, Yi Pan, Yuchen Jiang, Junjun Liu, Xianming Sun, Xianyun Wang, Caiyong Liu, Xingyu Chang, Zhaohua Zhao, Guangzhe Tapia, Juan Gonzalez-Soler, Lazaro Aravena, Carlos Schulz, Daniel 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 Norway ID R&D Inc. New York United States 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

关键词： Competition

Generalised tangent stabilised nonlinear elasticity: An automated framework for controlling material and geometric instabilities

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arXiv 2024年

作者： Poya, Roman Ortigosa, Rogelio Gil, Antonio J. Kim, Theodore Bonet, Javier Meshing Framework Simulation & Test Solutions Siemens Digital Industries Software Cambridge United Kingdom Multiphysics Simulation and Optimization Lab Technical University of Cartagena Cartagena Murcia Spain Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University Bay Campus United Kingdom Computer Graphics Group Yale School of Engineering & Applied Science Yale University New HavenCT United States Barcelona Spain

Tangent stabilised large strain isotropic elasticity was recently proposed by Poya et al. [1] wherein by working directly with principal stretches the entire eigenstructure of constitutive and geometric/initial stiffness terms were found in closed-form, giving fresh insights into exact convexity conditions of highly non-convex functions in discrete settings. Consequently, owing to these newly found tangent eigenvalues an analytic tangent stabilisation was proposed (for common non-convex strain energies that exhibit material and/or geometric instabilities) bypassing incumbent numerical approaches routinely used in nonlinear finite element analysis. This formulation appears to be extremely robust for quasi-static simulation of complex deformations even with no load increments and time stepping while still capturing instabilities (similar to dynamic analysis) automatically in ways that are infeasible for path-following techniques in practice. In this work, we generalise the notion of tangent stabilised elasticity to virtually all known invariant formulations of nonlinear elasticity. We show that, closed-form eigen-decomposition of tangents is easily available irrespective of invariant formulation or integrity basis. In particular, we work out closed-form tangent eigensystems for isotropic Total Lagrangian deformation gradient (F)-based and right Cauchy-Green (C)-based as well as Updated Lagrangian left Cauchy-Green (b)-based formulations and present their exact convexity conditions postulated in terms of their corresponding tangent and initial stiffness eigenvalues. In addition, we introduce the notion of geometrically stabilised polyconvex large strain elasticity for models that are materially stable but exhibit geometric instabilities for whom we construct their initial stiffness in a spectrally-decomposed form analytically. We further extend this framework to the case of transverse isotropy where once again, closed-form tangent eigensystems are found for common tran

关键词： Stiffness

The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias*

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The Unconstrained Ear Recognition Challenge 2023: Maximizing...

IEEE International Joint Conference on Biometrics (IJCB)

作者： Ž. Emeršić T. Ohki M. Akasaka T. Arakawa S. Maeda M. Okano Y. Sato A. George S. Marcel I. I. Ganapathi S. S. Ali S. Javed N. Werghi S. G. Işık E. Sarıtaş H. K. Ekenel V. Hudovernik J. N. Kolf F. Boutros N. Damer G. Sharma A. Kamboj A. Nigam D. K. Jain G. Cámara-Chávez P. Peer V. Štruc University of Ljubljana (UL SI) Shizuoka University (SU JP) Idiap Research Institute (IDIAP CH) Electrical Engineering and Computer Science Department Khalifa University (KU UAE) Department of Computer Engineering (ITU TR) Istanbul Technical University Microelectronic Guidance and Electro-Optical Group ASELSAN Fraunhofer Institute for Computer Graphics Research IGD (IGD DE) Technische Universität Darmstadt (TUD DE) Indian Institute of Technology Mandi (IIT IN) HCL Imaging and Robotics R&D Lab (HCL IN) School of Artificial Intelligence Dalian University of Technology (DLUT CN) Federal University of Ouro Preto (UFOP BR)

The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://***/

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Cross-View Image Retrieval - Ground to Aerial Image Retrieval through Deep Learning

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arXiv 2020年

作者： Khurshid, Numan Hanif, Talha Tharani, Mohbat Taj, Murtaza Computer Vision and Graphics Lab School of Science and Engineering Lahore University of Management Sciences Pakistan

Cross-modal retrieval aims to measure the content similarity between different types of data. The idea has been previously applied to visual, text, and speech data. In this paper, we present a novel cross-modal retrieval method specifically for multi-view images, called Cross-view Image Retrieval CVIR. Our approach aims to find a feature space as well as an embedding space in which samples from street-view images are compared directly to satellite-view images (and vice-versa). For this comparison, a novel deep metric learning based solution"DeepCVIR" has been proposed. Previous cross-view image datasets are deficient in that they (1) lack class information;(2) were originally collected for cross-view image geolocalization task with coupled images;(3) do not include any images from off-street locations. To train, compare, and evaluate the performance of cross-view image retrieval, we present a new 6 class cross-view image dataset termed as CrossViewRet which comprises of images including freeway, mountain, palace, river, ship, and stadium with 700 high-resolution dual-view images for each class. Results show that the proposed DeepCVIR outperforms conventional matching approaches on CVIR task for the given dataset and would also serve as the baseline for future research. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Attention Neural Network for Trash Detection on Water Channels

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arXiv 2020年

作者： Tharani, Mohbat Amin, Abdul Wahab Maaz, Mohammad Taj, Murtaza Computer Vision and Graphics Lab School of Science and Engineering Lahore University of Management Sciences Lahore Pakistan

Rivers and canals flowing through cities are often used illegally for dumping trash. This contaminates fresh water channels as well as causes blockage in sewerage resulting urban flooding. When this contaminated water reaches agricultural fields, it results in degradation of soil and poses critical environmental as well as economic threats. The dumped trash is often found floating on the water surface. The trash could be disfigured, partially submerged, decomposed into smaller pieces, clumped together with other objects which obscures its shape and creates a challenging detection problem. This paper proposes a method for detection of visible trash floating on the water surface of the canals in urban areas. We also provide a large dataset, first of its kind, trash in water channels that contains object level annotations. A novel attention layer is proposed that improves the detection of smaller objects. Towards the end of this paper, we provide a detailed comparison of our method with state-of-the-art object detectors and show that our method significantly improves the detection of smaller objects. The dataset will be made publicly available. Copyright © 2020, The Authors. All rights reserved.

关键词： Water quality

Automatic diagnosis of disc herniation based on DenseNet fusion model 8

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Automatic diagnosis of disc herniation based on DenseNet fus...

8th International Conference on Digital Home, ICDH 2020

作者： Wang, Zimin Qin, Junyan Huang, Jinrong Wang, Yumeng Li, Ji Guilin University of Electronic Technology Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Image and Graphics Guilin541004 China Guilin Huigu Institute of Artificial Intelligence Industrial Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9781728192345

Spine MRI images generally have the characteristics of low contrast and much noise. Because the variable shape of the spine edge, the traditional spine image segmentation method requires a lot of preprocessing and can not perform image segmentation automatically, which is inefficient and cannot compliant the clinical work requirements. So as to solve the above problems, this paper use deep learning algorithms to identify the spine images, and proposed a disc herniation recognition method for spinal MRI images which based on DenseNet fusion model. Use DenseNet and Inception-Resnet-V2 model fusion to get more image features and improve the image recognition accuracy. Experiments prove that this method can be used to identify the herniation of spinal intervertebral disc with high accuracy and real-time. © 2020 IEEE.

关键词： Image segmentation