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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

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. © 2023, CC BY-NC-SA.

关键词： Competition

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

The Unconstrained Ear Recognition Challenge 2023: Maximizing...

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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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Kvc-Ongoing: Keystroke Verification Challenge

SSRN

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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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Lithography-free Plasmonic color printing with Femtosecond laser on Semicontinuous silver films

arXiv

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arXiv 2020年

作者： Chowdhury, Sarah Nahar Nyga, Piotr Kudyshev, Zhaxylyk A. Bravo, Esteban Garcia Lagutchev, Alexei S. Kildishev, Alexander V. Shalaev, Vladimir M. Boltasseva, Alexandra School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue University 1205 W State St West LafayetteIN47907 United States Institute of Optoelectronics Military University of Technology 2 Kaliskiego St Warsaw00-908 Poland Department of Computer Graphics Technology Purdue University Knoy Hall 401 N. Grant St West LafayetteIN47907 United States

Plasmonic color printing with semicontinuous metal films is a lithography-free and environment-friendly method for generating non-fading bright colors. Such films are comprised of islands – metal nanoparticles and their clusters of various dimensions, which resonate at different wavelengths upon external light illumination depending on the size and shape of the local particle structures. To experimentally realize systems that were optimized for achieving a broad color range and increased stability, various silver semicontinuous metal films were deposited on a metallic (Ag) mirror with a sub-wavelength-thick dielectric (SiO2) spacer. Femtosecond laser post-processing was then introduced to extend the color gamut through spectrally induced changes from blue to green, red, and yellow. Long-term stability and durability of the structures were addressed to enable non-fading colors with an optimized overcoating dielectric layer. The thickness of the proposed design is on the order of 100 nanometers, and it can be deposited on any substrate. These structures generate a broad range of long-lasting colors in reflection that can be applied to real-life artistic or technological applications with a spatial resolution on the order of 0.3 mm or less. Copyright © 2020, The Authors. All rights reserved.

关键词： Color

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A graphic view of linear algebra

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computer-Aided Design 2000年第4期32卷 295-295页

作者： A Fischer Technion—Israel Institute of Technology Faculty of Mechanical Engineering CMSR Laboratory for Computer Graphics and CAD Haifa Israel

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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

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PictureSensation - a mobile application to help the blind explore the visual world through touch and sound

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Journal of rehabilitation and assistive technologies engineering 2016年 3卷 2055668316674582-2055668316674582页

作者： Michael Banf Ruben Mikalay Baris Watzke Volker Blanz Media Systems Group Institute for Vision and Graphics Department of Computer Science and Electrical Engineering University of Siegen Germany.

We present PictureSensation, a mobile application for the hapto-acoustic exploration of images. It is designed to allow for the visually impaired to gain direct perceptual access to images via an acoustic signal. PictureSensation introduces a swipe-gesture based, speech-guided, barrier free user interface to guarantee autonomous usage by a blind user. It implements a recently proposed exploration and audification principle, which harnesses exploration methods that the visually impaired are used to from everyday life. In brief, a user explores an image actively on a touch screen and receives auditory feedback about its content at his current finger position. PictureSensation provides an extensive tutorial and training mode, to allow for a blind user to become familiar with the use of the application itself as well as the principles of image content to sound transformations, without any assistance from a normal-sighted person. We show our application's potential to help visually impaired individuals explore, interpret and understand entire scenes, even on small smartphone screens. Providing more than just verbal scene descriptions, PictureSensation presents a valuable mobile tool to grant the blind access to the visual world through exploration, anywhere.

关键词： Visually impaired accessibility computer vision human computer interaction mobile computing

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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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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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Tutorial 1: Foundations and Practical Design of CMOS Image Sensors

Tutorial 1: Foundations and Practical Design of CMOS Image S...

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： Angel Rodríguez-Vázquez Hamada Alshaer Bayan Sharif Amr Fahim Eman El Mandouh Ashraf Salem Fadi A. Aloul Hoda S. Abdel-Aty John F. Dodge Baker Mohammad Hisham Mohamed IMSE-CNM Universidad de Sevilla/CSIC AnaFocus Ltd Sevilla Spain Etisalat BT Innovation Center (EBTIC) Khalifa University Abu Dhabi UAE Electrical and Computer Engineering Dean of the College of Engineering at Khalifa Abu Dhabi UAE Semtech Corporation Irvine CA Mentor Graphics Cairo Egypt Department of Computer Science & Engineering American University in Sharjah U.A.E. Oakland University Rochester MI USA Khalifa University of Science Technology & Research (KUSTAR) UAE Rensselaer Polytechnic Institute Troy NY USA

CMOS imagers are complex systems whose design requires quite different pieces of expertise, namely: pixels, analog signal processing, pixel readout and analog-to-digital conversion, digital signal processing, output drivers, etc. Confronting the design of new imagers require hence the concourse of multidisciplinary teams. However, because correct operation calls for the close interconnection among the different parts, global knowledge is mandatory for successful design. This is particularly pertinent for the newer generations of smart imagers required for high-end applications and/or requiring ultra-high image capture, on-chip image correction, scene interpretation, high dynamic range capture, etc. All these features demand architectural and circuital innovations and pose significant challenges to designers. Also, the increased interest on sensors capable of capturing 3-D scenes raise new challenges at circuit level related to the necessity to interface pixels different from those employed for 2-D capture, on the one hand, and to extract and convert to digital domain time information, on the other hand. This tutorial addresses the design of smart CMOS imagers by following a comprehensive and complete top-down approach where each subsystem is contemplated and described as a part of a whole. Starting the formulation of the performance metrics used to specify and characterize imagers, the tutorial explains how the subsystem behavior and non-idealities impact on the global imager metrics, thereby setting the basis to specify the subsystems for given global image sensor specs. Such methodology is illustrated in the tutorial via a dedicated, MATLAB-based modeling tool which will be employed to allow the attendees gaining insight on the impact of non-ideal sub-systems behaviors. The tutorial overviews the state-of-the-art regarding: pixels; analog signal processing and read-out circuitry; data conversion circuitry, covering both amplitude data converters (required for 2-D

关键词： Tutorials Sensors Wireless sensor networks Energy harvesting Abstracts Educational institutions Cognitive radio

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