IET Biometrics最新文献_第8页

Time-frequency fusion learning for photoplethysmography biometric recognition 光体积脉搏波生物识别的时频融合学习

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-04-12 DOI: 10.1049/bme2.12070

Chunying Liu, Jijiang Yu, Yuwen Huang, Fuxian Huang

引用次数: 1

Using double attention for text tattoo localisation 利用双重注意力进行文字纹身定位

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-04-08 DOI: 10.1049/bme2.12071

Xingpeng Xu, S. Prasad, Kuanhong Cheng, A. Kong

引用次数: 1

Using double attention for text tattoo localisation 利用双重注意力进行文字纹身定位

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-04-08 DOI: 10.1049/bme2.12071

Xingpeng Xu, Shitala Prasad, Kuanhong Cheng, Adams Wai Kin Kong

{"title":"Using double attention for text tattoo localisation","authors":"Xingpeng Xu, Shitala Prasad, Kuanhong Cheng, Adams Wai Kin Kong","doi":"10.1049/bme2.12071","DOIUrl":"https://doi.org/10.1049/bme2.12071","url":null,"abstract":"Text tattoos contain rich information about an individual for forensic investigation. To extract this information, text tattoo localisation is the first and essential step. Previous tattoo studies applied existing object detectors to detect general tattoos, but none of them considered text tattoo localisation and they neglect the prior knowledge that text tattoos are usually inside or nearby larger tattoos and appear only on human skin. To use this prior knowledge, a prior knowledge-based attention mechanism (PKAM) and a network named Text Tattoo Localisation Network based on Double Attention (TTLN-DA) are proposed. In addition to TTLN-DA, two variants of TTLN-DA are designed to study the effectiveness of different prior knowledge. For this study, NTU Tattoo V2, the largest tattoo dataset and NTU Text Tattoo V1, the largest text tattoo dataset are established. To examine the importance of the prior knowledge and the effectiveness of the proposed attention mechanism and the networks, TTLN-DA and its variants are compared with state-of-the-art object detectors and text detectors. The experimental results indicate that the prior knowledge is vital for text tattoo localisation; The PKAM contributes significantly to the performance and TTLN-DA outperforms the state-of-the-art object detectors and scene text detectors.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 3","pages":"199-214"},"PeriodicalIF":2.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91813390","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

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 : 2022-04-04 DOI: 10.1049/bme2.12072

Christian Rathgeb, Daniel Fischer, Pawel Drozdowski, Christoph Busch

{"title":"Reliable detection of doppelgängers based on deep face representations","authors":"Christian Rathgeb, Daniel Fischer, Pawel Drozdowski, Christoph Busch","doi":"10.1049/bme2.12072","DOIUrl":"https://doi.org/10.1049/bme2.12072","url":null,"abstract":"Doppelgängers (or lookalikes) usually yield an increased probability of false matches in a facial recognition system, as opposed to random face image pairs selected for non-mated comparison trials. In this work, the impact of doppelgängers on the HDA Doppelgänger and Disguised Faces in The Wild databases is assessed using a state-of-the-art face recognition system. It is found that doppelgänger image pairs yield very high similarity scores resulting in a significant increase of false match rates. Further, a doppelgänger detection method is proposed, which distinguishes doppelgängers from mated comparison trials by analysing differences in deep representations obtained from face image pairs. The proposed detection system employs a machine learning-based classifier, which is trained with generated doppelgänger image pairs utilising face morphing techniques. Experimental evaluations conducted on the HDA Doppelgänger and Look-Alike Face databases reveal a detection equal error rate of approximately 2.7% for the task of separating mated authentication attempts from doppelgängers.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 3","pages":"215-224"},"PeriodicalIF":2.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91797536","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

Profile to frontal face recognition in the wild using coupled conditional generative adversarial network 利用耦合条件生成对抗网络对野外侧面人脸进行识别

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-03-10 DOI: 10.1049/bme2.12069

Fariborz Taherkhani, Veeru Talreja, Jeremy Dawson, Matthew C. Valenti, Nasser M. Nasrabadi

{"title":"Profile to frontal face recognition in the wild using coupled conditional generative adversarial network","authors":"Fariborz Taherkhani, Veeru Talreja, Jeremy Dawson, Matthew C. Valenti, Nasser M. Nasrabadi","doi":"10.1049/bme2.12069","DOIUrl":"https://doi.org/10.1049/bme2.12069","url":null,"abstract":"In recent years, with the advent of deep-learning, face recognition (FR) has achieved exceptional success. However, many of these deep FR models perform much better in handling frontal faces compared to profile faces. The major reason for poor performance in handling of profile faces is that it is inherently difficult to learn pose-invariant deep representations that are useful for profile FR. In this paper, the authors hypothesise that the profile face domain possesses a latent connection with the frontal face domain in a latent feature subspace. The authors look to exploit this latent connection by projecting the profile faces and frontal faces into a common latent subspace and perform verification or retrieval in the latent domain. A coupled conditional generative adversarial network (cpGAN) structure is leveraged to find the hidden relationship between the profile and frontal images in a latent common embedding subspace. Specifically, the cpGAN framework consists of two conditional GAN-based sub-networks, one dedicated to the frontal domain and the other dedicated to the profile domain. Each sub-network tends to find a projection that maximises the pair-wise correlation between the two feature domains in a common embedding feature subspace. The efficacy of the authors’ approach compared with the state of the art is demonstrated using the CFP, CMU Multi-PIE, IARPA Janus Benchmark A, and IARPA Janus Benchmark C datasets. Additionally, the authors have also implemented a coupled convolutional neural network (cpCNN) and an adversarial discriminative domain adaptation network (ADDA) for profile to frontal FR. The authors have evaluated the performance of cpCNN and ADDA and compared it with the proposed cpGAN. Finally, the authors have also evaluated the authors’ cpGAN for reconstruction of frontal faces from input profile faces contained in the VGGFace2 dataset.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 3","pages":"260-276"},"PeriodicalIF":2.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91822997","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: 2

Profile to frontal face recognition in the wild using coupled conditional generative adversarial network 利用耦合条件生成对抗网络对野外侧面人脸进行识别

IF 2 4区计算机科学

IET Biometrics Pub Date : 2022-03-10 DOI: 10.1049/bme2.12069

Fariborz Taherkhani, Veeru Talreja, J. Dawson, M. Valenti, N. Nasrabadi

引用次数: 2

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 Moroń, Krzysztof Bernacki, Jerzy Fiołka, Jia Peng, Adam Popowicz

{"title":"Recognition of the finger vascular system using multi-wavelength imaging","authors":"Tomasz Moroń, Krzysztof Bernacki, Jerzy Fiołka, Jia Peng, Adam Popowicz","doi":"10.1049/bme2.12068","DOIUrl":"https://doi.org/10.1049/bme2.12068","url":null,"abstract":"There has recently been intensive development of methods for identification and personal verification using the human finger vascular system (FVS). The primary focus of these efforts has been the increasingly sophisticated methods of image processing, and frequently employing machine learning. In this article, we present a new concept of imaging in which the finger vasculature is illuminated using different wavelengths of light, generating multiple FVS images. We hypothesised that the analysis of these image sets, instead of individual images, could increase the effectiveness of identification. Analyses of data from over 100 volunteers, using five different deterministic methods for feature extraction, consistently demonstrated improved identification efficiency with the addition of data obtained from another wavelength. The best results were seen for combinations of diodes between 800 and 900 nm. Finger vascular system observations outside this range were of marginal utility. The knowledge gained from this experiment can be utilised by designers of biometric recognition devices leveraging FVS technology. Our results confirm that developments in this field are not restricted to image processing algorithms, and that hardware innovations remain relevant.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"11 3","pages":"249-259"},"PeriodicalIF":2.0,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2.12068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91803974","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

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