IEEE transactions on biometrics, behavior, and identity science最新文献

{"title":"IrisSPT: Cancelable Iris Template for Secure Authentication Based on Self-Parameterized Transform","authors":"Qianrong Zheng;Jianwen Xiang;Zhi Yao;Jing Tian;Huanhuan Li;Dongdong Zhao","doi":"10.1109/TBIOM.2025.3557530","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3557530","url":null,"abstract":"Biometrics have received significant attention as a result of the increasingly widespread adoption of biometric authentication systems by government and commercial organizations as an alternative to traditional password or token authentication methods. Existing cancellable biometric techniques typically transform feature vectors into protection templates by generating random transformation functions. However, the transformation functions of such cancellable biometric systems are prone to leakage, which in turn may lead to security risks. For this purpose, a novel Self-Parameterized Transformation (SPT) method is proposed in this paper. Specifically, by generating the transformation parameters directly from the iris data, the method effectively prevents the attacker from establishing a recognizable mapping relationship between the original iris data and the generated template, which makes it extremely complicated to recover the original iris data. In addition, to ensure irreversibility, this method avoids storing the transformation parameters on the client or server side, thus effectively preventing the attacker from reconstructing the original iris data through reverse engineering. Although it is possible to attempt to recover the original data by brute-force cracking, this process is hardly feasible. To achieve unlinkability, this paper further employs an aggregation function, which effectively prevents correlation leakage between templates. Theoretical analysis shows that the proposed SPT method is adequately guaranteed in terms of irreversibility, revocability, unlinkability, and performance retaining criteria in cancellable biometrics. Benchmarking results show that the proposed SPT method improves at least 23.4% on the global measure <inline-formula> <tex-math>$(D_{leftrightarrow }^{sys})$ </tex-math></inline-formula> compared to the existing schemes. Extensive experiments on CASIA-IrisV3-Interval, CASIA-IrisV4-Thousand and IIT Delhi Iris datasets demonstrate that the SPT approach has significant security advantages in biometric data protection and exhibits superior performance over state-of-the-art schemes. These results show that the proposed SPT method not only meets the stringent requirements in privacy protection, but also successfully realizes the balance between security and performance, and has high practical value.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"751-769"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480567","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}

{"title":"Gradient-Reversed Domain-Generalizable Multi-Modal Cross-Attention Network for Robust Face Anti-Spoofing","authors":"Koyya Deepthi Krishna Yadav;Ilaiah Kavati;Ramalingaswamy Cheruku","doi":"10.1109/TBIOM.2025.3616651","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3616651","url":null,"abstract":"Face recognition systems are increasingly vulnerable to presentation attacks, wherein adversaries employ artifacts such as printed photographs, replayed videos, or 3D masks, etc, to impersonate genuine users. Despite significant progress in face anti-spoofing, most existing methods exhibit poor generalization when confronted with unseen attack types or domain shifts such as changes in sensors, environments, or acquisition protocols, thereby limiting their robustness in real-world applications. We propose the Gradient-Reversed Domain-Generalizable Multi-Modal Cross-Attention Network (GR-DXNet) that enhances resilience against previously unseen attacks and enables seamless adaptation across diverse domains. GR-DXNet employs dual-modality learning by fusing RGB frames, which capture fine-grained texture, with depth maps that reveal 3D structural cues. Temporal Convolutional Networks (TCNs) are integrated to model motion-based inconsistencies, improving the detection of dynamic emerging spoof patterns. To enhance cross-modal representation, a query-key-value-based cross-attention mechanism is introduced, enabling effective alignment and fusion of RGB and depth features. Furthermore, a post-fusion Gradient Reversal Layer (GRL) is employed that adversarially aligns cross-modal embeddings to suppress domain-specific bias without handcrafted augmentations or complex disentanglement, encouraging the model to learn domain-invariant features and strengthens generalization under unseen domains. Extensive evaluations on benchmark datasets across both intra- and cross-dataset protocols, offering a reliable solution for real-world face spoof detection.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"8 1","pages":"84-98"},"PeriodicalIF":5.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045306","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}

{"title":"Practical Biometric Search Under Encryption: Meeting the NIST Runtime Requirement Without Loss of Accuracy","authors":"Amina Bassit;Florian F. W. Hahn;Raymond N. J. Veldhuis;Andreas Peter","doi":"10.1109/TBIOM.2025.3545751","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3545751","url":null,"abstract":"Biometric search consists of comparing a biometric probe with biometric references in a database to find identities that match the probe. In addition to being resource-intensive, biometric search can lead to privacy violations when biometric data is exposed. To protect the sensitive biometric data, state-of-the-art search solutions are based on fully homomorphic encryption (FHE). However, they either reduce search accuracy to gain efficiency or lack efficiency and fail to meet the 10s NIST-FRVT requirement for a one-in-a-million search. In this paper, we present a fast and accurate biometric search solution under encryption for biometric data represented as vectors, a commonly used compact representation for compressing high-dimensional data. Our search solution is exhaustive and does not require any pre-selection (e.g., indexing) or dimensionality reduction techniques, which can degrade search accuracy. Also, it is parallelizable and supports dynamic databases. Our efficiency gain stems from our chunking and vertical organization of the encrypted reference database. This enables us to optimally leverage the single-instruction multiple-data (SIMD) property of FHE schemes. For a database of one million encrypted references represented by 512-dimensional vectors and a security level of 128 bits, our solution runs a fully encrypted exhaustive search in 9.23s measured on a 64-bit computer Intel Xeon Platinum 8358 with 64 CPUs and 64GB RAM. Hence, our solution is the first to satisfy the 10s NIST-FRVT limit under encryption, outperforming the state-of-the-art solutions by more than two orders of magnitude (at least 246 times faster) while achieving an accuracy improvement of at least 4.6% FNIR for a fixed FPIR at 0.1%.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"710-727"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902488","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480563","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}

{"title":"FRNS: A Dual-Strategy Framework for Fine-Grained Feature Refinement and Text Noise Suppression in Text-Based Person Retrieval","authors":"Shijuan Huang;Zongyi Li;Hefei Ling;Xu Li;Linnan Tu","doi":"10.1109/TBIOM.2025.3582434","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3582434","url":null,"abstract":"Text-based person retrieval aims to retrieve images of a specific individual based on textual descriptions. Recent approaches concentrate on aligning global and local features across text and image modalities. However, these methods struggle to obtain precise fine-grained image features and often ignore the noise in textual descriptions due to the disruption from the images’ background and the unmatched words in text. To alleviate these problems, we propose the Fine-grained Feature Refinement and Text Noise Suppression (FRNS) framework. This innovative solution consists of two key components: the Fine-grained Feature Refinement (FFR) module, designed to enhance the precision of local image features through the extraction of foreground information and the removal of background noise; and the Text Noise Suppression (TNS) module, which effectively mitigates the impact of irrelevant or misleading textual information by identifying and masking unmatched words with high probability. The experimental results demonstrate the effectiveness of our FRNS method on three widely-used datasets: CUHK-PEDES (Li et al., 2017), ICFG-PEDES (Ding et al., 2021) and RSTPReid (Zhu et al., 2021). Our code will be available on GitHub at <uri>https://github.com/ShijuanHuang/FRNS</uri>.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"588-596"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480118","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}

{"title":"Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition","authors":"Shanaka Ramesh Gunasekara;Wanqing Li;Philip O. Ogunbona;Jie Yang","doi":"10.1109/TBIOM.2025.3566212","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3566212","url":null,"abstract":"Traditional approaches in unsupervised or self-supervised learning for skeleton-based action classification have concentrated predominantly on the dynamic aspects of skeletal sequences. Yet, the intricate interaction between the moving and static elements of the skeleton presents a rarely tapped discriminative potential for action classification. This paper introduces a novel measurement, referred to as spatial-temporal joint density (STJD), to quantify such interaction. Tracking the evolution of this density throughout an action can effectively identify a subset of discriminative moving and/or static joints termed prime joints to steer self-supervised learning. A new contrastive learning strategy named STJD-CL is proposed to align the representation of a skeleton sequence with that of its prime joints while simultaneously contrasting the representations of prime and non-prime joints. In addition, a method called STJD-MP is developed by integrating it with a reconstruction-based framework for more effective learning. Experimental evaluations on the NTU RGB+D 60, NTU RGB+D 120, and PKUMMD datasets in various downstream tasks demonstrate that the proposed STJD-CL and STJD-MP improved performance, particularly by 3.5 and 3.6 percentage points over the state-of-the-art contrastive methods on the NTU RGB+D 120 dataset using X-sub and X-set evaluations, respectively. The code is available at STJD.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"632-642"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479429","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}

{"title":"A Unified Framework for Segmentation, Scaling, and Indexing of Contactless Fingerprints","authors":"Javad Khodadoust;Raúl Monroy;Emanuela Marasco","doi":"10.1109/TBIOM.2025.3575652","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3575652","url":null,"abstract":"Contactless fingerprint identification is gaining prominence due to its convenience, accessibility, security, and hygiene benefits over traditional contact-based methods. However, achieving accurate identification and efficiently searching large-scale databases remain challenging. In this paper, we introduce CFinSegNet, an encoder-decoder network for fingerprint segmentation that operates on preprocessed fingerprints and multiple image representations from diverse color spaces rather than the original fingerprint images. Additionally, we present FreqFinScale, a no-reference regression-based model designed to handle scaling variations using frequency representations. Furthermore, we propose FreqFinIndex, a deep neural network architecture for indexing and retrieving contactless fingerprints, which compresses high-dimensional frequency domain data into a compact latent space for fast and accurate search. Our experimental evaluation on five publicly available datasets demonstrates that our method significantly outperforms state-of-the-art approaches in accuracy and efficiency.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"668-680"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480621","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}

{"title":"Micro-Expression Classification With Weighted Locally Constrained Graph Network","authors":"Ankith Jain Rakesh Kumar;Bir Bhanu","doi":"10.1109/TBIOM.2025.3550430","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3550430","url":null,"abstract":"Micro-expressions are non-verbal communication cues that are often brief and subtle that make their recognition difficult. Despite the progress made by deep learning-based methods, especially graph-based approaches, micro-expression classification still faces challenges in detecting their intricate features. Graph neural networks (GNNs) often suffer from oversmoothing problems, where node features become overly similar during iterative message passing, resulting in reduced discriminative capacity and an inability to comprehend the graph’s diverse structural information. To address these limitations, this paper introduces a novel weighted edge-node locally constrained graph attention network (WLC-GAT) which uses Dirichlet energy that is automatically applied to node features and global/local edge features within a graph attention network, ensuring feature distinctiveness for each class and preserving crucial information about the relationships between nodes and related edge structures. The paper utilizes a sliding window optical flow approach for the automated selection of high-intensity expression frames from videos and selects varying patch sizes across each landmark point determined by optical flow data to extract better features. To capture spatiotemporal information a three-frame graph structure is used. Finally, a dual-channel WLC-GAT network fusion focuses on discovering correlations among features. Empirical experiments demonstrate that the proposed approach consistently outperforms existing methods on three micro-expression databases (SAMM, CASME II, and SMIC) by significant margins on both single and composite databases.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"695-709"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478819","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}

{"title":"Voice2Visage: Deciphering Faces From Voices","authors":"Wuyang Chen;Kele Xu;Yanjie Sun;Yong Dou;Huaimin Wang","doi":"10.1109/TBIOM.2025.3615961","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3615961","url":null,"abstract":"The human voice carries valuable cues about an individual’s identity and emotions. A more intriguing question emerges: can one’s facial appearance be deduced from their voice alone? Existing efforts have primarily focused on exploring the relationship between natural audio and visual data, with limited attention given to the specific biometric domain of speaker-voice and face correlation. This study seeks to model the facial-related information embedded within the voice and ultimately predict an unknown person’s appearance solely based on their unheard voice. This task presents several challenges: firstly, while natural sounds exhibit significant variability, human voices often share similar frequencies, complicating the establishment of mappings between them. Secondly, generating faces from a voice presents an ill-posed problem, as details such as makeup and heap pose cannot be inferred from voice alone. In this article, we introduce a novel framework named Voice2Visage, designed to tackle this task by leveraging self-supervised cross-modal and intra-modal learning to predict faces corresponding to input voice. To ensure the feasibility of our method, we optimize existing algorithms in automated dataset collection. Additionally, we systematically design experiments to test the usability and stability of commonly used quantitative metrics in the field of facial identity comparison. The results validate the close semantic association between the generated face and the reference one, showcasing its reliability. Our work provides a fresh perspective on exploring the depth of physiological characteristics concealed within human voices and the intricate interplay between appearance and voice. Our code is available at <uri>https://github.com/colaudiolab/Voice2Visage</uri>.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"8 1","pages":"111-121"},"PeriodicalIF":5.0,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045358","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}

{"title":"Structural Consistency for Face Forgery Detection via Frequency Domain Enhancement and Self-Predictive Learning","authors":"Lifang Zhou;Miaomiao Chen;Hangsheng Ruan","doi":"10.1109/TBIOM.2025.3614578","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3614578","url":null,"abstract":"Existing face forgery detection methods often overfit to known forgery patterns, resulting in limited generalization to unseen manipulations. To address this issue, we propose a Structural Consistency method for Face Forgery Detection via Frequency Domain Enhancement and Self-predictive Learning, which embeds frequency-domain features into the spatial representation and leverages the structural consistency inherent in genuine facial contexts to provide more discriminative cues for forgery identification. Specifically, we design a data augmentation module to extract the frequency information through the Discrete Cosine Transform (DCT) and enhance it using a Frequency Domain Enhancement Module (FEM) to capture subtle forgery artifacts. Furthermore, we design a Self-Prediction Learning Module (SPLM) that reconstructs the occluded central region of a face by exploiting the structural consistency of real facial features. To further guide the learning process, we define a self-predictive reconstruction loss that minimizes the prediction error in the occluded region and helps reinforce structural consistency. Moreover, we propose a Reconstruction Difference Guidance (RDG) module, which explicitly emphasizes potential forgery regions by computing pixel-wise discrepancies between the reconstructed image and the original input. This process produces an attention map that guides the classifier to focus on semantically inconsistent or anomalous regions. Experimental results demonstrate that our method achieves superior generalization and robustness across diverse datasets.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"8 1","pages":"99-110"},"PeriodicalIF":5.0,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045300","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}

{"title":"IEEE Transactions on Biometrics, Behavior, and Identity Science Information for Authors","authors":"","doi":"10.1109/TBIOM.2025.3607046","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3607046","url":null,"abstract":"","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"C3-C3"},"PeriodicalIF":5.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11180156","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134932","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}