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arXiv 2021年

作者： Fu, Biying Kirchbuchner, Florian Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Due to the COVID-19 situation, face masks have become a main part of our daily life. Wearing mouth-and-nose protection has been made a mandate in many public places, to prevent the spread of the COVID-19 virus. However, face masks affect the performance of face recognition, since a large area of the face is covered. The effect of wearing a face mask on the different components of the face recognition system in a collaborative environment is a problem that is still to be fully studied. This work studies, for the first time, the effect of wearing a face mask on face image quality by utilising state-of-theart face image quality assessment methods of different natures. This aims at providing better understanding on the effect of face masks on the operation of face recognition as a whole system. In addition, we further studied the effect of simulated masks on face image utility in comparison to real face masks. We discuss the correlation between the mask effect on face image quality and that on the face verification performance by automatic systems and human experts, indicating a consistent trend between both factors. The evaluation is conducted on the database containing (1) no-masked faces, (2) real face masks, and (3) simulated face masks, by synthetically generating digital facial masks on no-masked faces. Finally, a visual interpretation of the face areas contributing to the quality score of a selected set of quality assessment methods is provided to give a deeper insight into the difference of network decisions in masked and non-masked faces, among other variations. Copyright © 2021, The Authors. All rights reserved.

关键词： Viruses

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ReGenMorph: Visibly realistic GAN generated face morphing attacks by attack re-generation

arXiv

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arXiv 2021年

作者： Damer, Naser Raja, Kiran Süßmilch, Marius Venkatesh, Sushma Boutros, Fadi Fang, Meiling Kirchbuchner, Florian Ramachandra, Raghavendra Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Germany Department of Computer Science TU Darmstadt Germany Norwegian University of Science and Technology Norway

Face morphing attacks aim at creating face images that are verifiable to be the face of multiple identities, which can lead to building faulty identity links in operations like border checks. While creating a morphed face detector (MFD), training on all possible attack types is essential to achieve good detection performance. Therefore, investigating new methods of creating morphing attacks drives the generalizability of MADs. Creating morphing attacks was performed on the image level, by landmark interpolation, or on the latent-space level, by manipulating latent vectors in a generative adversarial network. The earlier results in varying blending artifacts and the latter results in synthetic-like striping artifacts. This work presents the novel morphing pipeline, ReGenMorph, to eliminate the LMA blending artifacts by using a GAN-based generation, as well as, eliminate the manipulation in the latent space, resulting in visibly realistic morphed images compared to previous works. The generated ReGenMorph appearance is compared to recent morphing approaches and evaluated for face recognition vulnerability and attack detectability, whether as known or unknown attacks. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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Removing fences from sweep motion videos using global 3D reconstruction and fence-aware light field rendering

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Computational Visual Media 2019年第1期5卷 21-32页

作者： Chanya Lueangwattana Shohei Mori Hideo Saito Department of Science and Technology Keio University Institute of Computer Graphics and Vision Graz University of Technology

Diminishing the appearance of a fence in an image is a challenging research area due to the characteristics of fences(thinness, lack of texture, etc.) and the need for occluded background restoration. In this paper, we describe a fence removal method for an image sequence captured by a user making a sweep motion, in which occluded background is potentially observed. To make use of geometric and appearance information such as consecutive images, we use two well-known approaches: structure from motion and light field rendering. Results using real image sequences show that our method can stably segment fences and preserve background details for various fence and background combinations. A new video without the fence, with frame coherence, can be successfully provided.

关键词： video fence video repair diminished reality(DR) structure from motion(SfM) light field rendering(LFR)

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Standard vs. non-standard cross-validation: evaluation of performance in a space with structured distribution of datapoints

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Procedia computer science 2021年 192卷 1245-1254页

作者： Grzegorz Baron Urszula Stańczyk 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

Cross-validation is a popularly used approach to evaluation of performance for classifiers. It relies on random selection of independent samples for training and testing, and assumes that if any similarities among samples exist, they do not lead to known grouping of datapoints in the input space. If these conditions are violated, as it may happen for datasets with some structure of samples included, standard cross-validation can return biased results even for many folds. In the paper the research on cross-validation was reported for application to stylometric datasets, describing a task of authorship attribution. The comparison of standard and non-standard processing was presented. In the latter case, selected subsets of examples were swapped over between training and test sets several times. The experiments with three popular classifiers showed that standard cross-validation tended to give over-optimistic results, whereas non-standard processing was more guarded, and by that more reliable. To avoid high computational costs involved, evaluation based on averaged predictions for limited numbers of test sets can be considered as a reasonable compromise.

关键词： Cross-validation Fold Evaluation of Performance Classification Distribution Stylometry

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Fairness in Face Presentation Attack Detection

arXiv

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arXiv 2022年

作者： Fang, Meiling Yang, Wufei Kuijper, Arjan Štruc, Vitomir Damer, Naser Fraunhofer Institute for Computer Graphics Research Darmstadt Germany College of Information Engineering Yangzhou University Yangzhou China Department of Computer Science TU Darmstadt Darmstadt Germany Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia

Face recognition (FR) algorithms have been proven to exhibit discriminatory behaviors against certain demographic and non-demographic groups, raising ethical and legal concerns regarding their deployment in real-world scenarios. Despite the growing number of fairness studies in FR, the fairness of face presentation attack detection (PAD) has been overlooked, mainly due to the lack of appropriately annotated data. To avoid and mitigate the potential negative impact of such behavior, it is essential to assess the fairness in face PAD and develop fair PAD models. To enable fairness analysis in face PAD, we present a Combined Attribute Annotated PAD Dataset (CAAD-PAD), offering seven human-annotated attribute labels. Then, we comprehensively analyze the fairness of PAD and its relation to the nature of the training data and the Operational Decision Threshold Assignment (ODTA) through a set of face PAD solutions. Additionally, we propose a novel metric, the Accuracy Balanced Fairness (ABF), that jointly represents both the PAD fairness and the absolute PAD performance. The experimental results pointed out that female and faces with occluding features (e.g. eyeglasses, beard, etc.) are relatively less protected than male and non-occlusion groups by all PAD solutions. To alleviate this observed unfairness, we propose a plug-and-play data augmentation method, FairSWAP, to disrupt the identity/semantic information and encourage models to mine the attack clues. The extensive experimental results indicate that FairSWAP leads to better-performing and fairer face PADs in 10 out of 12 investigated cases. CAAD-PAD is publicly available 1 © 2022, CC BY-NC-SA.

关键词： Face recognition

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Weighting factor for attributes based on reduct cardinality

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Procedia computer science 2021年 192卷 1255-1264页

作者： Urszula Stańczyk 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

Estimation of attribute importance can be obtained by a mechanism that allows to assign some weight to variables. Weighting attributes can lead to their ordering, which, in turn, can be exploited for feature selection and reduction. Decision reducts constitute an example of a mechanism aiming at dimensionality reduction, embedded in rough set approach to data mining. The paper presents research works, where the process of weighting was driven by the proposed factor based on reducts, with varying their sets, and the results were analysed through the perspective of reduct cardinality, since it is typically considered as the most significant indicator of reduct quality. Constructed rankings of variables were used for inferring sets of decision rules from gradually decreasing numbers of features, and then the performance of the rule classifiers was tested. The experiments show that for the weighting factor to be useful for feature reduction, not only reduct cardinalities, but also the numbers of reducts found need to be taken into account.

关键词： Weighting Ranking Feature selection Rough set theory Decision reduct Reduct cardinality

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Automation of the Accuracy of Short-Term Financial Planning and Forecasting the Activities of Credit

Automation of the Accuracy of Short-Term Financial Planning ...

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2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020

作者： Osipov, A.V. Faleeva, E.V. Pavlova, K.T. Far Eastern State Transport University Khabarovsk Russia Far Eastern State Transport University Department of Computer Science and Computer Graphics Khabarovsk Russia

ISBN: (纸本)9781728169514

This article examines the possibility of implementing methods of short-term forecasting by means of small automation. The main direction of the research is the effectiveness of methods in conditions of an extremely limited resource for the development and implementation of a software package. The implementation of an automated system that makes it possible to promptly make a forecast with the necessary and sufficient accuracy, taking into account the influence of external factors, but at the same time, it is flexible and easily corrected without the involvement of a specialized team of developers will significantly reduce the time for making a management decision without reducing the quality and efficiency of the adopted solutions. The possibility of using small automation tools and the effectiveness of this approach in comparison with the 'expert assessment' method are considered. © 2020 IEEE.

关键词： Automation

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Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis

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NeuroImage 2025年 314卷 121256-121256页

作者： Qin, Jianmei Wu, Haoting Wu, Chenqing Guo, Tao Zhou, Cheng Duanmu, Xiaojie Tan, Sijia Wen, Jiaqi Zheng, Qianshi Yuan, Weijin Zhu, Zihao Chen, Jingwen Wu, Jingjing He, Chenyu Ma, Yiran Liu, Chunlei Xu, Xiaojun Guan, Xiaojun Zhang, Minming Department of Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Joint Laboratory of Clinical Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China State Key Laboratory of Computer-aided Design & Computer Graphics Zhejiang University College of Computer Science and technology Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University College of Control Science and Engineering Hangzhou China Department of Electrical Engineering and Computer Sciences University of California Berkeley CA United States Helen Wills Neuroscience Institute University of California Berkeley CA United States

Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson's disease. A total of 321 participants (148 patients with Parkinson's Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), *** consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson's disease and normal controls. RSFC-based machine learning models were constructed for Parkinson's disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87–0.90, FUNC2T1: 0.67–0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinso

关键词： Parkinson's disease Quantitative susceptibility mapping Resting-state functional connectivity Subcortical nuclei

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HEALTH DISPARITIES THROUGH GENERATIVE AI MODELS: A COMPARISON STUDY USING A DOMAIN SPECIFIC LARGE LANGUAGE MODEL

arXiv

引用

arXiv 2023年

作者： Bautista, Yohn Jairo Parra Lima, Vinicious Theran, Carlos Aló, Richard Computing Information Science Department Florida A&M University TallahasseeFL32317 United States Computer Graphics Department Purdue University West LafayetteIN47906 United States College of Science and Technology Florida A&M University TallahasseeFL32317 United States

Health disparities are differences in health outcomes and access to healthcare between different groups, including racial and ethnic minorities, low-income people, and rural residents. An artificial intelligence (AI) program called large language models (LLMs) can understand and generate human language, improving health communication and reducing health disparities. There are many challenges in using LLMs in human-doctor interaction, including the need for diverse and representative data, privacy concerns, and collaboration between healthcare providers and technology experts. We introduce the comparative investigation of domain-specific large language models such as SciBERT with a multi-purpose LLMs BERT. We used cosine similarity to analyze text queries about health disparities in exam rooms when factors such as race are used alone. Using text queries, SciBERT fails when it doesn’t differentiate between queries text: "race" alone and "perpetuates health disparities." We believe clinicians can use generative AI to create a draft response when communicating asynchronously with patients. However, careful attention must be paid to ensure they are developed and implemented ethically and equitably. © 2023, CC BY.

关键词： Health care

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Visual Analysis of Point Cloud Neighborhoods via Multi-Scale Geometric Measures

Visual Analysis of Point Cloud Neighborhoods via Multi-Scale...

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42nd Annual Conference on European Association for computer graphics, EUROgraphics 2021

作者： Ritter, M. Schiffner, D. Harders, M. Interactive Graphics and Simulation Group The Department of Computer Science University of Innsbruck Austria DIPF Leibniz Institute for Research and Information Education Frankfurt Germany Airborne Hydromapping GmbH Innsbruck Austria

ISBN: (纸本)9783038681335

Point sets are a widely used spatial data structure in computational and observational domains, e.g. in physics particle simulations, computer graphics or remote sensing. Algorithms typically operate in local neighborhoods of point sets, for computing physical states, surface reconstructions, etc. We present a visualization technique based on multi-scale geometric features of such point clouds. We explore properties of different choices on the underlying weighted co-variance neighborhood descriptor, illustrated on different point set geometries and for varying noise levels. The impact of different weighting functions and tensor centroids, as well as point set features and noise levels becomes visible in the rotation-invariant feature images. We compare to a curvature based scale space visualization method and, finally, show how features in real-world LiDAR data can be inspected by images created with our approach in an interactive tool. In contrast to the curvature based approach, with our method line structures are highlighted over growing scales, with clear border regions to planar or spherical geometric structures. © 2021 The Author(s).

关键词： Data visualization

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