检索结果-内蒙古大学图书馆

IEEE Workshop on Applications of computer Vision (WACV)

作者： Marco Huber Anh Thi Luu Philipp Terhörst Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany Paderborn University Paderborn Germany

Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications. Understanding why two face images are matched or not matched by a given face recognition system is important to operators, users, and developers to increase trust, accountability, develop better systems, and highlight unfair behavior. In this work, we propose a similarity score argument backpropagation (xSSAB) approach that supports or opposes the face-matching decision to visualize spatial maps that indicate similar and dissimilar areas as interpreted by the underlying FR model. Furthermore, we present Patch-LFW, a new explainable face verification benchmark that enables along with a novel evaluation protocol, the first quantitative evaluation of the validity of similarity and dissimilarity maps in explainable face recognition approaches. We compare our efficient approach to state-of-the-art approaches demonstrating a superior trade-off between efficiency and performance. The code as well as the proposed Patch-LFW is publicly available at: https://***/marcohuber/xSSAB.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Efficiency of the Machine Learning Methods for Gesture Recognition Using Double-Channel Electromyography 8

Comparative Analysis of Efficiency of the Machine Learning M...

引用

8th International Young Scientists Conference on Information Technologies, Telecommunications and Control Systems, ITTCS 2021

作者： Ponomarchuk, Y.V. Kuznetsov, I.V. Computer Science and Computer Graphics Department Far Eastern State Transport University Khabarovsk680000 Russia

The paper is devoted to the efficiency analysis of the machine learning methods for gesture recognition, which are applied to the surface double-channel electromyography data. The comparative analysis was conducted for recognition of eight types of palm movements. The results of the analysis lead to conclusion that it is necessary to consider the muscle groups' location for better recognition accuracy and the increase of the number of considered gestures. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation

arXiv

引用

arXiv 2024年

作者： Babnik, Žiga Boutros, Fadi Damer, Naser Peer, Peter Štruc, Vitomir University of Ljubljana Ljubljana Slovenia Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Face Image Quality Assessment (FIQA) techniques have seen steady improvements over recent years, but their performance still deteriorates if the input face samples are not properly aligned. This alignment sensitivity comes from the fact that most FIQA techniques are trained or designed using a specific face alignment procedure. If the alignment technique changes, the performance of most existing FIQA techniques quickly becomes suboptimal. To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures. To validate the proposed distillation approach, we conduct comprehensive experiments on 6 face datasets with 4 recent face recognition models and in comparison to 7 state-of-the-art FIQA techniques. Our results show that AI-KD consistently improves performance of the initial FIQA techniques not only with misaligned samples, but also with properly aligned facial images. Furthermore, it leads to a new state-of-the-art, when used with a competitive initial FIQA approach. The code for AI-KD is made publicly available from: https://***/LSIbabnikz/AI-KD. © 2024, CC BY.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation

AI-KD: Towards Alignment Invariant Face Image Quality Assess...

引用

International Workshop on Biometrics and Forensics (IWBF)

作者： Žiga Babnik Fadi Boutros Naser Damer Peter Peer Vitomir Štrue University of Ljubljana Ljubljana Slovenia Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

ISBN: (数字)9798350354478

ISBN: (纸本)9798350354485

关键词：

来源：评论

学校读者我要写书评

暂无评论

Weighting Attributes Based on the Greedy Algorithm Properties

引用

Procedia computer science 2024年 246卷 4883-4892页

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

Estimation of importance for considered features is an important issue for any knowledge exploration process and it can be executed by a variety of approaches. In the research reported in this study, the primary aim was the development of a methodology for creating attribute rankings. Based on the properties of the greedy algorithm for inducing decision rules, a new application of this algorithm has been proposed. Instead of constructing a single ordering of features, attributes were weighted multiple times. The input datasets were discretised with several algorithms representing supervised and unsupervised discretisation approaches. Each resulting discrete data variant was exploited to construct a ranking of attributes. The effectiveness of the obtained rankings was confirmed through a rule filtering process governed by weighted attributes. The methodology was applied to the stylometric task of authorship attribution. The experimental outcomes demonstrate the value of the proposed research method, as it generally led to improved predictions while taking into account a noticeably decreased sets of attributes and decision rules.

关键词： Greedy algorithm Weighting attributes Decision rules Rule filtering Authorship attribution

来源：评论

学校读者我要写书评

暂无评论

ExFaceGAN: Exploring Identity Directions in GAN’s Learned Latent Space for Synthetic Identity Generation

ExFaceGAN: Exploring Identity Directions in GAN’s Learned L...

引用

IEEE International Joint Conference on Biometrics (IJCB)

作者： Fadi Boutros Marcel Klemt Meiling Fang Arjan Kuijper Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Deep generative models have recently presented impressive results in generating realistic face images of random synthetic identities. To generate multiple samples of a certain synthetic identity, previous works proposed to disentangle the latent space of GANs by incorporating additional supervision or regularization, enabling the manipulation of certain attributes. Others proposed to disentangle specific factors in unconditional pretrained GANs latent spaces to control their output, which also requires supervision by attribute classifiers. Moreover, these attributes are entangled in GAN’s latent space, making it difficult to manipulate them without affecting the identity information. We propose in this work a framework, ExFaceGAN, to disentangle identity information in pretrained GANs latent spaces, enabling the generation of multiple samples of any synthetic identity. Given a reference latent code of any synthetic image and latent space of pretrained GAN, our ExFaceGAN learns an identity directional boundary that disentangles the latent space into two sub-spaces, with latent codes of samples that are either identity similar or dissimilar to a reference image. By sampling from each side of the boundary, our ExFaceGAN can generate multiple samples of synthetic identity without the need for designing a dedicated architecture or supervision from attribute classifiers. We demonstrate the generalizability and effectiveness of ExFaceGAN by integrating it into learned latent spaces of three SOTA GAN approaches. As an example of the practical benefit of our ExFaceGAN, we empirically prove that data generated by ExFaceGAN can be successfully used to train face recognition models (https://***/fdbtrs/ExFaceGAN).

关键词：

来源：评论

学校读者我要写书评

暂无评论

Architecture proposal for adding FOG processing capability to a Smart Building Management System

Architecture proposal for adding FOG processing capability t...

引用

International Conference on Fog and Mobile Edge Computing (FMEC)

作者： David Martínez Casas Andrea Rey Presas José M. Cotos Yáñez José A. Taboada González Electronics and Computer Science Department Computer Graphics and Data Engineering (COGRADE) Research Group University of Santiago de Compostela Santiago de Compostela Spain

ISBN: (数字)9798350366488

ISBN: (纸本)9798350366495

This article examines the current state of the intelligent building monitoring system created for the University of Santiago de Compostela (USC) in the framework of the OPERE project and proposes a modification based on the Fog Computing paradigm. The study is developed in the context of the European regulations for the energy efficiency of facilities and the reduction of greenhouse gases. The current system implements the data processing in DADIS modules, developed by this research group for the acquisition and flexible transmission of information. These modules provide the information to the monitoring system which offers functionalities such as energy consumption dashboards, configurable operation schedules and ad hoc data visualization. However, the limitations of the current system include the difficulty in scaling the processing of the acquired information and database queries. The described upgrade proposes the extensive use of MQTT to standardize communications and allow the development of stand-alone applications to scale processing. The same architecture facilitates the incorporation of Big Data infrastructures that would solve even more complex query problems than those addressed in this scenario.

关键词： Schedules Smart buildings Multi-access edge computing Architecture Europe computer architecture Regulation Proposals Greenhouse gases Monitoring

来源：评论

学校读者我要写书评

暂无评论

Bias and Diversity in Synthetic-based Face Recognition

arXiv

引用

arXiv 2023年

作者： Huber, Marco Luu, Anh Thi Boutros, Fadi Kuijper, Arjan Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face images of people who do not exist. However, it is a known and sensitive problem that face recognition systems are susceptible to bias, i.e. performance differences between different demographic and non-demographics attributes, which can lead to unfair decisions. In this work, we investigate how the diversity of synthetic face recognition datasets compares to authentic datasets, and how the distribution of the training data of the generative models affects the distribution of the synthetic data. To do this, we looked at the distribution of gender, ethnicity, age, and head position. Furthermore, we investigated the concrete bias of three recent synthetic-based face recognition models on the studied attributes in comparison to a baseline model trained on authentic data. Our results show that the generator generate a similar distribution as the used training data in terms of the different attributes. With regard to bias, it can be seen that the synthetic-based models share a similar bias behavior with the authentic-based models. However, with the uncovered lower intra-identity attribute consistency seems to be beneficial in reducing bias. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection

arXiv

引用

arXiv 2023年

作者： Huber, Marco Fang, Meiling Boutros, Fadi Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Face recognition (FR) systems continue to spread in our daily lives with an increasing demand for higher explainability and interpretability of FR systems that are mainly based on deep learning. While bias across demographic groups in FR systems has already been studied, the bias of explainability tools has not yet been investigated. As such tools aim at steering further development and enabling a better understanding of computer vision problems, the possible existence of bias in their outcome can lead to a chain of biased decisions. In this paper, we explore the existence of bias in the outcome of explainability tools by investigating the use case of face presentation attack detection. By utilizing two different explainability tools on models with different levels of bias, we investigate the bias in the outcome of such tools. Our study shows that these tools show clear signs of gender bias in the quality of their explanations. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

On the Quality and Diversity of Synthetic Face Data and its Relation to the Generator Training Data

On the Quality and Diversity of Synthetic Face Data and its ...

引用

International Workshop on Biometrics and Forensics (IWBF)

作者： Biying Fu Marcel Klemt Fadi Boutros Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

In recent years, advances in deep learning techniques and large-scale identity-labeled datasets have enabled facial recognition algorithms to rapidly gain performance. However, due to privacy issues, ethical concerns, and regulations governing the processing, transmission, and storage of biometric samples, several publicly available face image datasets are being withdrawn by their creators. The reason is that these datasets are mostly crawled from the web with the possibility that not all users had properly consented to processing their biometric data. To mitigate this problem, synthetic face images from generative approaches are motivated to substitute the need for authentic face images to train and test face recognition. In this work, we investigate both the relation between synthetic face image data and the generator authentic training data and the relation between the authentic data and the synthetic data in general under two aspects, i.e. the general image quality and face image quality. The first term refers to perceived image quality and the second measures the utility of a face image for automatic face recognition algorithms. To further quantify these relations, we build the analyses under two terms denoted as the dissimilarity in quality values expressing the general difference in quality distributions and the dissimilarity in quality diversity expressing the diversity in the quality values.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：