IEEE transactions on biometrics, behavior, and identity science最新文献_第9页

Improving Fingerprint Presentation Attack Detection by an Approach Integrated Into the Personal Verification Stage 一种集成到个人验证阶段的方法改进指纹呈现攻击检测

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-04-15 DOI: 10.1109/TBIOM.2025.3560692

Marco Micheletto;Giulia Orrù;Luca Ghiani;Gian Luca Marcialis

{"title":"Improving Fingerprint Presentation Attack Detection by an Approach Integrated Into the Personal Verification Stage","authors":"Marco Micheletto;Giulia Orrù;Luca Ghiani;Gian Luca Marcialis","doi":"10.1109/TBIOM.2025.3560692","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3560692","url":null,"abstract":"Presentation Attack Detection (PAD) systems are usually designed independently of the fingerprint verification system. While this can be acceptable for use cases where specific user templates are not predetermined, it represents a missed opportunity to enhance security in scenarios where integrating PAD with the fingerprint verification system could significantly leverage users’ templates, which are the real target of a potential presentation attack. This does not mean that a PAD should be specifically designed for such users; that would imply the availability of many enrolled users’ PAI and, consequently, complexity, time, and cost increase. On the contrary, we propose to equip a basic PAD, designed according to the state of the art, with an innovative add-on module called the Closeness Binary Code (CC) module. The term “closeness” refers to a peculiar property of the bona fide-related features: in an Euclidean feature space, genuine fingerprints tend to cluster in a specific pattern. First, samples from the same finger are close to each other, then samples from other fingers of the same user and finally, samples from fingers of other users. This property is statistically verified in our previous publication, and further confirmed in this paper. It is independent of the user population and the feature set class, which can be handcrafted or deep network-based (embeddings). Therefore, the add-on can be designed without the need for the targeted user samples; moreover, it exploits her/his samples’ “closeness” property during the verification stage. Extensive experiments on benchmark datasets and state-of-the-art PAD methods confirm the benefits of the proposed add-on, which can be easily coupled with the main PAD module integrated into the fingerprint verification system.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"654-667"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Frequency-Assisted Temporal Upsampling Artifacts Representation Learning for Face Forgery Detection 频率辅助时域上采样伪影表征学习用于人脸伪造检测

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-03-13 DOI: 10.1109/TBIOM.2025.3569645

Rui Sun;Xiaolu Yu;Fei Wang;Zikai Da;Yifan Zhang;Jun Gao

{"title":"Frequency-Assisted Temporal Upsampling Artifacts Representation Learning for Face Forgery Detection","authors":"Rui Sun;Xiaolu Yu;Fei Wang;Zikai Da;Yifan Zhang;Jun Gao","doi":"10.1109/TBIOM.2025.3569645","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3569645","url":null,"abstract":"Recently, the highly realistic deepfake images have aroused the potential for deepfake abuse, and both the detection and its generalization remain challenging. Existing deepfake video detection methods strive to capture distinguishing features between fake and real faces through temporal modeling. However, these works provide local supervision between sampled video frames, but overlook intra-frame generative artifacts, which can function as an useful indicators for detection. To mitigate the issue, we propose a temporal-frequential convolutional network based on the representation of upsampling artifacts, which can automatically capture incoherent artifacts representation in different ends. Specifically, we design the Upsampling Artifacts Representation Module (UARM), which captures upsampling artifacts introduced in generation end based on the relationship between pixels, and then we probe the deep coupling of intra-frame global frequency information with inter-frame incoherence, and design the Frequency-Assisted Temporal Incoherence Module (FATIM), which can facilitate the detection of temporal information in RGB domain by frequency domain information, and the Self-tuned Convolutions Module (SCM) is designed to supplement information and adjust it automatically in the learning process. In addition, the Information Fusion Module (IFM) is designed to couple multiple information and establish a more comprehensive representation. With the above modules, our proposed method can learn robust features in both generation end and detection end. Extensive experiments demonstrate that our method outperforms other state-of-the-art video detection methods by a large margin on the challenging DFDC and FF++ in low-quality.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"728-739"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Homomorphically Encrypted Biometric Template Fusion and Matching 同态加密生物特征模板融合与匹配

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-08-04 DOI: 10.1109/TBIOM.2025.3595438

Ramin Akbari;Luke Sperling;Nalini K. Ratha;Arun Ross;Vishnu Naresh Boddeti

{"title":"Homomorphically Encrypted Biometric Template Fusion and Matching","authors":"Ramin Akbari;Luke Sperling;Nalini K. Ratha;Arun Ross;Vishnu Naresh Boddeti","doi":"10.1109/TBIOM.2025.3595438","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3595438","url":null,"abstract":"Biometric fusion is a promising method to elevate the recognition performance of unimodal biometric systems. Nevertheless, the exposure of feature vectors for feature-level fusion raises security concerns, as it is feasible to extract sensitive information from these vectors. This paper proposes a non-interactive, end-to-end approach to securely fuse and match biometric templates using Fully Homomorphic Encryption (FHE). For a pair of encrypted feature vectors, we perform the following operations on a ciphertext domain: i) feature concatenation, ii) fusion and dimensionality reduction through a learned linear projection, iii) an optional scale normalization to unit <inline-formula> <tex-math>$ell _{2}$ </tex-math></inline-formula>-norm, and iv) match score computation. Our method, dubbed HEFT, is custom-designed to circumvent a key limitation of FHE - the lack of support for non-arithmetic operations. From an inference perspective, we systematically explore different data packing schemes for computationally efficient linear projection and introduce a polynomial approximation for scale normalization. From a training perspective, we introduce two distinct FHE-aware algorithms to improve the learning of the projection matrix and address the challenges posed by the non-arithmetic normalization step. We demonstrate the utility of HEFT on two multimodal combinations: face and voice and face and fingerprint. For the face-voice fusion, HEFT improves verification performance by a range of 143.25% - 244.35% compared to unibiometric features. On the fingerprint-face fusion, improvements are from 13.99% to 37.99%. Code and data are available at <uri>https://github.com/human-analysis/encrypted-biometric-fusion</uri> <uri>https://github.com/human-analysis/encrypted-biometric-fusion</uri>.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"573-587"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11111688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148482329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pain Level Estimation From Videos by Analyzing the Dynamics of Facial Landmarks With a Spatio-Temporal Graph Neural Network 基于时空图神经网络的面部特征动态分析视频疼痛程度估计

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-07-28 DOI: 10.1109/TBIOM.2025.3592836

Fatemah Alhamdoosh;Pietro Pala;Stefano Berretti

{"title":"Pain Level Estimation From Videos by Analyzing the Dynamics of Facial Landmarks With a Spatio-Temporal Graph Neural Network","authors":"Fatemah Alhamdoosh;Pietro Pala;Stefano Berretti","doi":"10.1109/TBIOM.2025.3592836","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3592836","url":null,"abstract":"Developing effective and accurate methods for automatic estimation of pain level is vital, particularly for monitoring individuals, such as newborns and patients in intensive care units, who cannot communicate verbally. This paper introduces a novel video-based approach for sequence-level pain estimation that addresses two primary challenges in the existing literature. Firstly, we address privacy concerns in methods that rely on full facial images, which expose patient identities, limiting their applicability in healthcare. Our approach uses facial landmarks that offer insights into facial expressions, while preserving privacy as they do not suffice for personal identification. Secondly, pain is a dynamic state with intensity varying over time. Our approach analyzes temporal features at short- and long-term levels, adapting to continuous frame sequences. In essence, we develop a regression model with two components: 1) A Short-term Dynamics Network, where a Spatio-temporal Attention Graph Convolution Network (STAGCN) extracts short-term features from a spatio-temporal graph constructed with nodes representing facial landmarks extracted from each frame, and 2) A Long-term Dynamics Network, where a Gated Recurrent Unit (GRU) processes the sequence of short-term features to learn long-term patterns across the entire sequence. We validated our approach using the BioVid Heat Pain dataset (Parts A, B, and D) and MIntPain assessing performance in multi-class and binary (pain vs. no pain) classifications. Results demonstrate the approach’s potential, even with partially occluded faces.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"610-619"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11097893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Biometrics, Behavior, and Identity Science Publication Information IEEE生物计量学、行为与身份科学学报

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-09-25 DOI: 10.1109/TBIOM.2025.3607045

引用次数: 0

AAPAR: CLIP-Based Adaptation and Alignment for Pedestrian Attribute Recognition 基于clip的行人属性识别自适应与对齐

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-06-30 DOI: 10.1109/TBIOM.2025.3584107

Xu Li;Hefei Ling;Yuxuan Shi;Zongyi Li;Ping Li

{"title":"AAPAR: CLIP-Based Adaptation and Alignment for Pedestrian Attribute Recognition","authors":"Xu Li;Hefei Ling;Yuxuan Shi;Zongyi Li;Ping Li","doi":"10.1109/TBIOM.2025.3584107","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3584107","url":null,"abstract":"Pedestrian Attribute Recognition (PAR) aims to identify attributes of target pedestrians in images. While current methods improve performance through attribute localization and correlation modeling, CLIP-based approaches demonstrate superior effectiveness by leveraging textual semantics to mine attribute relationships. However, two critical limitations persist: <xref>(1)</xref> existing CLIP adaptation methods in feature extraction networks fail to adequately bridge the gap between CLIP’s design purpose (single-label modality alignment) and PAR’s task nature (multi-label recognition with inter-attribute correlations); and <xref>(2)</xref> inadequate exploitation of attribute hierarchies. To address these challenges, we propose AAPAR, a CLIP-based adaptation and alignment model with two key contributions: <xref>(1)</xref> a modality-aligned CLIP adaptation method that integrates Transformer layers as adaptation modules after the image encoder while preserving crucial pre-adaptation image-attribute caption alignment, jointly enhancing multi-label dependencies while preserving original alignment knowledge; and <xref>(2)</xref> a hierarchical classification architecture comprising global-context, part-aware local, and refined individual attribute classifiers that collectively model attribute relationships at multiple granularities. Extensive experiments on standard PAR datasets (PETA, PA-100K, and RAPv2) and UPAR2024 dataset demonstrate that AAPAR outperforms state-of-the-art methods. The source code will be released on <uri>https://github.com/lixusky/AAPAR</uri>.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"643-653"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Level Feature Perception Network for Set-Based Video Face Identification 基于集的视频人脸识别多级特征感知网络

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-02-24 DOI: 10.1109/TBIOM.2025.3545273

Xizhan Gao;Xingrui Liu;Wei Hu;Sijie Niu;Zihao Dong;Fa Zhu;Danilo Pelusi

{"title":"Multi-Level Feature Perception Network for Set-Based Video Face Identification","authors":"Xizhan Gao;Xingrui Liu;Wei Hu;Sijie Niu;Zihao Dong;Fa Zhu;Danilo Pelusi","doi":"10.1109/TBIOM.2025.3545273","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3545273","url":null,"abstract":"As an important biometric technology, video face identification has broad application prospects and has been widely concerned by researchers for a long time. Although many video face identification methods have emerged, they still have not fully overcome the inherent problems such as intra-video diversity, inter-video similarity. In view of this, a set-based video face identification method called Multi-level Feature Perception Network (MFPN) is proposed, which consists of two modules: a video feature generation module and a distance metric learning module. These modules fully consider the cohesiveness within each video and the similarity between different videos. At first, the video feature generation module is used to learn (or perceive) the atom-level features of each frame, and in this process, the cohesiveness problem within each video is considered to facilitate the subsequent learning of compact video features. Then, the distance metric learning module is utilized to further learn (or perceive) the deep concept-level features of videos taking into account the similarity between different videos to enhance the discriminative ability of these features. Finally, the class of the test video is determined based on its deep concept-level features. Experimental results on the Honda, MoBo, YTC, and YTF video face datasets show that the proposed method significantly improves recognition rates in both video face identification and video face verification tasks.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"794-807"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE T-BIOM Editorial Board Changes IEEE T-BIOM编辑委员会的变化

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-09-25 DOI: 10.1109/TBIOM.2025.3611764

Mark Nixon

引用次数: 0

Behavioral Fingerprints for User Identification via Unsupervised Matching, Supervised Learning of Data Augmentation, and Their Fusion 基于无监督匹配、数据增强的监督学习及其融合的用户行为指纹识别

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-07-10 DOI: 10.1109/TBIOM.2025.3587435

Can Yang;Yingying Zhu;Ngai Cheong

{"title":"Behavioral Fingerprints for User Identification via Unsupervised Matching, Supervised Learning of Data Augmentation, and Their Fusion","authors":"Can Yang;Yingying Zhu;Ngai Cheong","doi":"10.1109/TBIOM.2025.3587435","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3587435","url":null,"abstract":"Behavioral fingerprints refer to the unique patterns and characteristics of user behaviors hidden in a number of activity traces generated in the digital space. It is difficult to forge and supports non-intrusive data collection, making it well-suited for user identification. However, the sparsity of behavioral data often leads to unsatisfactory identification performance. This paper adopts the frequency of user access to content items as the behavioral fingerprint and proposes two data augmentation methods to address this issue. On one hand, we update the unsupervised behavioral fingerprinting method from SURE to SURE+, which utilizes the union of n-level extendable multi-item-set fingerprints to preserve more user features. On the other hand, we have designed a convolutional neural network that is trained on mixup data, surpassing the performance of models trained solely on original data. Additionally, we have constructed a fusion decision-making model based on the outputs of unsupervised methods and supervised learning approaches to further enhance identification performance. Experimental results from three distinct datasets demonstrate that our proposed methods significantly outperform existing approaches in terms of identification accuracy. Especially, we achieved close to 100% accuracy on small-scale datasets with high frequency access.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"597-609"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Offline Signature Verification: Exploring Intra-Variability Across Time Intervals 离线签名验证：跨时间间隔探索内部变异性

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-07-25 DOI: 10.1109/TBIOM.2025.3592880

Kumari Priya;Chandranath Adak;Shivam Anand;Soumi Chattopadhyay

引用次数: 0