IET Biometrics最新文献_第9页

Recognition of the finger vascular system using multi-wavelength imaging 多波长成像识别手指血管系统

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-03-05 DOI: 10.1049/bme2.12068

Tomasz Moron, K. Bernacki, J. Fiolka, Jia Peng, A. Popowicz

引用次数: 1

Corresponding keypoint constrained sparse representation three-dimensional ear recognition via one sample per person 对应的关键点约束稀疏表示三维人耳识别，每个人一个样本

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-03-02 DOI: 10.1049/bme2.12067

Qinping Zhu, Zhichun Mu, Li Yuan

引用次数: 2

Corresponding keypoint constrained sparse representation three-dimensional ear recognition via one sample per person 对应的关键点约束稀疏表示三维人耳识别，每个人一个样本

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-03-02 DOI: 10.1049/bme2.12067

Qi Zhu, Zhichun Mu, Li Yuan

引用次数: 2

Proposing a Fuzzy Soft-max-based classifier in a hybrid deep learning architecture for human activity recognition 在混合深度学习架构中提出一种基于模糊soft -max的分类器，用于人类活动识别

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-02-06 DOI: 10.1049/bme2.12066

Reza Shakerian, Meisam Yadollahzadeh-Tabari, Seyed Yaser Bozorgi Rad

{"title":"Proposing a Fuzzy Soft-max-based classifier in a hybrid deep learning architecture for human activity recognition","authors":"Reza Shakerian, Meisam Yadollahzadeh-Tabari, Seyed Yaser Bozorgi Rad","doi":"10.1049/bme2.12066","DOIUrl":"10.1049/bme2.12066","url":null,"abstract":"Human Activity Recognition (HAR) is the process of identifying and analysing activities performed by a person (or persons). This paper proposes an efficient HAR system based on wearable sensors that uses deep learning techniques. The proposed HAR takes the advantage of staking Convolutional Neural Network and Long Short-Term (LSTM), for extracting the high-level features of the sensors data and for learning the time-series behaviour of the abstracted data, respectively. This paper proposed a Fuzzy Soft-max classifier for the dense layer which classifies the output of LSTM Blocks to the associated activity classes. The authors’ decision for proposing this classifier was because sensor data related to the resembling human activities, such as walking and running or opening door and closing door, are often very similar to each other. For this reason, the authors expect that adding fuzzy inference power to the standard Soft-max classifier will increase its accuracy for distinguishing between similar activities. The authors were also interested in considering a post-processing module that considers activity classification over a longer period. Using the proposed Fuzzy Soft-max classifier and by the post-processing technique, the authors were able to reach the 97.03 and 85.1 rates of accuracy for the PAMAP2 and Opportunity dataset, respectively.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 2","pages":"171-186"},"PeriodicalIF":2.0,"publicationDate":"2022-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84241402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Reliable Detection of Doppelgängers based on Deep Face Representations 基于深度人脸表征的Doppelgängers可靠检测

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-01-21 DOI: 10.1049/bme2.12072

C. Rathgeb, Daniel Fischer, P. Drozdowski, C. Busch

引用次数: 1

Deep learning model based on cascaded autoencoders and one-class learning for detection and localization of anomalies from surveillance videos 基于级联自编码器和单类学习的深度学习模型用于监控视频异常的检测和定位

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-01-18 DOI: 10.1049/bme2.12064

Karishma Pawar, Vahida Attar

{"title":"Deep learning model based on cascaded autoencoders and one-class learning for detection and localization of anomalies from surveillance videos","authors":"Karishma Pawar, Vahida Attar","doi":"10.1049/bme2.12064","DOIUrl":"10.1049/bme2.12064","url":null,"abstract":"Due to the need for increased security measures for monitoring and safeguarding the activities, video anomaly detection is considered as one of the significant research aspects in the domain of computer vision. Assigning human personnel to continuously check the surveillance videos for finding suspicious activities such as violence, robbery, wrong U-turns, to mention a few, is a laborious and error-prone task. It gives rise to the need for devising automated video surveillance systems ensuring security. Motivated by the same, this paper addresses the problem of detection and localization of anomalies from surveillance videos using pipelined deep autoencoders and one-class learning. Specifically, we used a convolutional autoencoder and a sequence-to-sequence long short-term memory autoencoder in a pipelined fashion for spatial and temporal learning of the videos, respectively. The authors followed the principle of one-class classification for training the model on normal data and testing it on anomalous testing data. The authors achieved a reasonably significant performance in terms of an equal error rate and the time required for anomaly detection and localization comparable to standard benchmarked approaches, thus, qualifies to work in a near-real-time manner for anomaly detection and localization.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 4","pages":"289-303"},"PeriodicalIF":2.0,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73763275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Signal-level fusion for indexing and retrieval of facial biometric data 基于信号级融合的面部生物特征数据索引与检索

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-01-13 DOI: 10.1049/bme2.12063

Pawel Drozdowski, Fabian Stockhardt, Christian Rathgeb, Christoph Busch

引用次数: 3

Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform 在移动平台上使用具有击键轨迹特征和递归神经网络的多模态方案的用户行为生物识别框架

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-01-11 DOI: 10.1049/bme2.12065

Ka-Wing Tse, Kevin Hung

{"title":"Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform","authors":"Ka-Wing Tse, Kevin Hung","doi":"10.1049/bme2.12065","DOIUrl":"10.1049/bme2.12065","url":null,"abstract":"Diverse applications are used on mobile devices. Because of the increasing dependence on information systems, immense amounts of personal and sensitive data are stored on mobile devices. Thus, security or privacy breaches are a major challenge. To protect mobile systems and the private information on these systems from being accessed by adversaries, a framework for mobile user identification through the use of a multimodal behavioural biometrics scheme with a keystroke trajectory feature is presented herein. Conventionally, mobile devices have been protected by mechanisms such as PINs or passwords. However, these approaches have numerous disadvantages. Therefore, approaches that employ keystroke biometrics for secure and reliable mobile device identification have been proposed. Because unimodal behavioural biometrics identification mechanisms have limited accuracy and effectiveness, a multimodal scheme that includes different behavioural biometric traits, such as keystroke and swipe biometric traits, is examined. However, the information provided by the spatial and temporal features of keystroke biometrics is not comprehensive. Therefore, a trajectory model is derived to describe the behavioural biometric uniqueness of a user. In the user identification phase, a multistream recurrent neural network (RNN) is adopted. The results reveal that the proposed trajectory model performs well, and the multimodal scheme using an RNN with a late fusion method provides accurate identification results. The proposed system achieved an accuracy of 95.29%, F1 score of 94.64%, and equal error rate of 1.78%. Thus, the proposed mobile identification system is capable of resisting attacks that standard mechanisms may be vulnerable to and represents a valuable contribution to cyber security.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 2","pages":"157-170"},"PeriodicalIF":2.0,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84484031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Spoofing detection on adaptive authentication System-A survey 自适应认证系统a调查中的欺骗检测

IF 2 4区计算机科学

IET Biometrics Pub Date : 2021-12-30 DOI: 10.1049/bme2.12060

Hind Baaqeel, S. O. Olatunji

引用次数: 2

Spoofing detection on adaptive authentication System-A survey 自适应认证系统a调查中的欺骗检测

IF 2 4区计算机科学

IET Biometrics Pub Date : 2021-12-30 DOI: 10.1049/bme2.12060

Hind Baaqeel, Sunday Olusanya Olatunji

{"title":"Spoofing detection on adaptive authentication System-A survey","authors":"Hind Baaqeel, Sunday Olusanya Olatunji","doi":"10.1049/bme2.12060","DOIUrl":"10.1049/bme2.12060","url":null,"abstract":"With the widespread of computing and mobile devices, authentication using biometrics has received greater attention. Although biometric systems usually provide efficient solutions, the recognition performance tends to be affected over time due to changing conditions and the ageing of biometric data, which results in intra-class variability. This issue is one of the leading causes of the high false rejection rate in biometric authentication systems. Fortunately, this issue has been addressed by using adaptive biometric solutions in which the system gradually adapts to new changes in user biometrics. However, their adaptability to changes may be exploited by an attacker to compromise the stored templates, either to impersonate a specific client or to deny access to him/her. In this work, the authors will carry out a systematic literature review by conducting a comparative study on state-of-the-art solutions for spoofing detection on adaptive authentication systems. This paper will identify the main issues that need to be addressed in adaptive authentication systems. Thus, the authors aim to encourage researchers to develop more robust adaptive solutions to overcome the identified gaps in this research.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 2","pages":"87-96"},"PeriodicalIF":2.0,"publicationDate":"2021-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12060","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"118570025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1