IET Biometrics最新文献_第2页

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2026-05-14

引用次数: 0

From Identification to Recognition: A Lesson for Biometric Technology From the History of Personal Identity 从识别到识别：从个人身份的历史给生物识别技术上的一课

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2026-05-06 DOI: 10.1049/bme2/3937059

Emilio Mordini, James L. Wayman

{"title":"From Identification to Recognition: A Lesson for Biometric Technology From the History of Personal Identity","authors":"Emilio Mordini, James L. Wayman","doi":"10.1049/bme2/3937059","DOIUrl":"https://doi.org/10.1049/bme2/3937059","url":null,"abstract":"Throughout the history of automated personal identification, now called “biometrics” in some communities, there has been controversy over its implications for personal privacy and human dignity. This controversy has been deepened by equivocation regarding the philosophical concept of personal identity, the social concept of recognition of persons, and the forensic concept of person identification. These concepts of personal identity, recognition, and identification are central to Western literature, shared traditions, and our notions of “self,” so it is not surprising that technologies dealing with these issues would elicit controversy. The assumption of this paper is that greater understanding of the cultural meanings and nuances of these concepts is necessary to illuminate some critical aspects of the current ethical and policy debate surrounding biometric technology. Accordingly, we attempt to deconfound these core concepts by differentiating persons from attributes and labels from notions of self. We explicate two different but interrelated forms of identity and discuss their connection to recognition, identifiers, and identification. Clear definitions are presented for each of these concepts. With the intent of enlightening the complicated relationship society and individuals have with biometric technology, we discuss social risks and suggest that the complexities of identity and recognition have always been important in Western tradition, as illustrated in the Biblical story of Abraham and the Greek story of Odysseus. We conclude that biometric technologies, although not without risks, have a legitimate place in modern society for assigning identifiers to persons based on body characteristics and should not be understood as extending to the philosophical and religious foundations of personal concepts of self.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/bme2/3937059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148068538","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: 0

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2026-05-06

引用次数: 0

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2026-04-03

引用次数: 0

Enhanced Face Recognition With Residual Networks and Dilated Contextual Transformer Attention 利用残差网络和扩展的语境变形注意增强人脸识别

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2026-04-03 DOI: 10.1049/bme2/7309163

Randa Nachet, Javier Garrigós, Tarik Boudghene Stambouli

{"title":"Enhanced Face Recognition With Residual Networks and Dilated Contextual Transformer Attention","authors":"Randa Nachet, Javier Garrigós, Tarik Boudghene Stambouli","doi":"10.1049/bme2/7309163","DOIUrl":"10.1049/bme2/7309163","url":null,"abstract":"Face recognition in unconstrained environments remains a challenging problem due to significant variations in facial appearance. In this article, a systematic investigation is conducted into the effectiveness of integrating attention mechanisms within a ResNet34 backbone. ResNet34 is adopted as a compact backbone compared to commonly used deeper ResNet variants, offering a good balance between representational capacity and computational cost. Specifically, three variants are examined: self-attention (SA), contextual transformer (CoT) attention, and the proposed dilated CoT (DCoT) attention. The primary contribution of this study is the demonstration that DCoT, by enlarging the receptive field without substantially increasing the number of parameters, constitutes a more suitable architectural choice for this backbone. This integration enables the network to capture broader contextual information and attend more effectively to discriminative facial features. The proposed models are trained on the CASIA-WebFace dataset and evaluated on standard benchmarks, including labled faces in the wild (LFW), CPLFW, and AgeDB_30. Extensive experiments show that the DCoT-enhanced model achieves consistent improvements over the baseline and other attention variants. Notably, the best-performing model attains an accuracy of 99.60% on the LFW dataset. These findings provide meaningful insights into the interaction between attention mechanisms and convolutional architectures, illustrating the efficacy of the proposed approach for face recognition in unconstrained, real-world conditions, while using a moderately sized backbone that balances capacity and parameter count.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2/7309163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147668069","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: 0

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2026-03-20

引用次数: 0

Bridging Domains in Fingerprint Recognition With Quality-Normalized Fusion 基于质量归一化融合的指纹识别桥接域

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2026-03-20 DOI: 10.1049/bme2/9281903

Xinwei Liu, Ning Fang, Peiyuan Zhang, Liangbin Zhang

{"title":"Bridging Domains in Fingerprint Recognition With Quality-Normalized Fusion","authors":"Xinwei Liu, Ning Fang, Peiyuan Zhang, Liangbin Zhang","doi":"10.1049/bme2/9281903","DOIUrl":"10.1049/bme2/9281903","url":null,"abstract":"Cross-domain fingerprint recognition remains difficult when latent impressions and smartphone finger photos are matched against contact-based sensor templates. We study this problem in the latent fingerprints in the wild (LFIW) database. We benchmark five representative matchers on eight latent- and finger-photo-to-sensor protocols. The results confirm a clear gap: no single matcher is robust across all domains. We then introduce two domain-aware components. First, domain-normalized quality score (DNQS) standardizes existing quality metrics within each domain. This reduces domain bias and improves the agreement between quality and verification error. Second, quality-weighted score fusion (QWSF) uses DNQS to fuse two complementary matchers, MCC and Neurotechnology. QWSF reduces errors across all protocols. It achieves a 15% relative equal error rate (EER) reduction on iPad vs. Opt and a 7% reduction on average. Paired bootstrap confidence intervals support the reliability of the gains. Improvements also hold at a low-FMR operating point (FNMR at FMR = 10−3). These findings show that quality normalization and quality-aware fusion can improve cross-domain robustness without retraining matchers.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2/9281903","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567345","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: 0

Frequency-Aware Cue Fusion and Ensembled Prompt Learning for Unified Face Attack Detection With Vision–Language Model 基于频率感知线索融合和集成提示学习的统一人脸攻击检测

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2026-03-08 DOI: 10.1049/bme2/1941529

Chaohao Jiang, Dang Li, Jing Gu, Yanyan Liang

{"title":"Frequency-Aware Cue Fusion and Ensembled Prompt Learning for Unified Face Attack Detection With Vision–Language Model","authors":"Chaohao Jiang, Dang Li, Jing Gu, Yanyan Liang","doi":"10.1049/bme2/1941529","DOIUrl":"10.1049/bme2/1941529","url":null,"abstract":"Unified face attack detection (UAD) systems, which utilize vision–language models to simultaneously discriminate between physical and digital attacks, remain challenging due to the difficulty in effectively identifying live information and the biological cues forged by a variety of distinct technologies. The difficulty stems from two primary aspects: (1) Single spatial information is insufficient for capturing comprehensive cues regarding both genuine and forged identities. (2) Textual prompts struggle to acquire low-redundancy and complementary information to facilitate the discrimination between live and fake cues. To address these issues, we propose a novel frequency-aware and ensembled text prompt UAD model built upon the contrastive language-image pretraining (CLIP) framework. Our model adaptively fuses spatial and frequency information to enhance the representation of genuine faces and all types of attacks, while simultaneously introducing an ensemble learning strategy to acquire low-redundancy textual prompts. Specifically, the ensemble prompt module generates general live and fake prompts from spatial and frequency information on the language branch, thus guiding the model to learn a unified feature space to deal with different attacks. Meanwhile, this module optimizes the redundancy and complementarity between prompts through an ensemble strategy and a designed information diversity constraint. Furthermore, we design a layer-wise cross-attention module in the vision branch to fuse frequency information from different layers. A designed redundancy minimization module is employed on the fused image features, thereby compelling the spatial and frequency feature extraction modules to generate maximally exclusive features. Extensive experiments on multiple benchmarks demonstrate that our model achieves state-of-the-art performance across most protocols of the datasets.","PeriodicalId":48821,"journal":{"name":"IET Biometrics","volume":"2026 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/bme2/1941529","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564153","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: 0

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2026-03-08

引用次数: 0

IF 1.8 4区计算机科学

IET Biometrics Pub Date : 2026-02-18

引用次数: 0