IEEE transactions on biometrics, behavior, and identity science最新文献_第8页

2025 Index IEEE Transactions on Biometrics, Behavior, and Identity Science 生物识别、行为与身份科学汇刊

IF 5

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-10 DOI: 10.1109/TBIOM.2025.3618315

引用次数: 0

Person Recognition in Aerial Surveillance: A Decade Survey 航空监视中的人识别：十年调查

IF 5

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-03 DOI: 10.1109/TBIOM.2025.3617011

K. Nguyen;Feng Liu;C. Fookes;S. Sridharan;Xiaoming Liu;Arun Ross

{"title":"Person Recognition in Aerial Surveillance: A Decade Survey","authors":"K. Nguyen;Feng Liu;C. Fookes;S. Sridharan;Xiaoming Liu;Arun Ross","doi":"10.1109/TBIOM.2025.3617011","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3617011","url":null,"abstract":"The rapid emergence of airborne platforms and imaging sensors is enabling new forms of aerial surveillance due to their unprecedented advantages in scale, mobility, deployment, and covert observation capabilities. This article provides a comprehensive overview of 150+ papers over the last 10 years of human-centric aerial surveillance tasks from a computer vision and machine learning perspective. It aims to provide readers with an in-depth systematic review and technical analysis of the current state of aerial surveillance tasks using drones, UAVs, and other airborne platforms. The object of interest is humans, where human subjects are to be detected, identified, and re-identified. More specifically, for each of these tasks, we first identify unique challenges in performing these tasks in an aerial setting compared to the popular ground-based setting and subsequently compile and analyze aerial datasets publicly available for each task. Most importantly, we delve deep into the approaches in the aerial surveillance literature with a focus on investigating how they presently address aerial challenges and techniques for improvement. We conclude the paper by discussing the gaps and open research questions to inform future research avenues.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"8 1","pages":"3-19"},"PeriodicalIF":5.0,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146045303","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

Robust Dynamic Facial Expression Recognition 鲁棒动态面部表情识别

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-03-03 DOI: 10.1109/TBIOM.2025.3546279

Feng Liu;Hanyang Wang;Siyuan Shen

{"title":"Robust Dynamic Facial Expression Recognition","authors":"Feng Liu;Hanyang Wang;Siyuan Shen","doi":"10.1109/TBIOM.2025.3546279","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3546279","url":null,"abstract":"The study of Dynamic Facial Expression Recognition (DFER) is a nascent field of research that involves the automated recognition of facial expressions in video data. Although existing research has primarily focused on learning representations under noisy and hard samples, the issue of the coexistence of both types of samples remains unresolved. In order to overcome this challenge, this paper proposes a robust method of distinguishing between hard and noisy samples. This is achieved by evaluating the prediction agreement of the model on different sampled clips of the video. Subsequently, methodologies that reinforce the learning of hard samples and mitigate the impact of noisy samples can be employed. Moreover, to identify the principal expression in a video and enhance the model’s capacity for representation learning, comprising a key expression re-sampling framework and a dual-stream hierarchical network is proposed, namely Robust Dynamic Facial Expression Recognition (RDFER). The key expression re-sampling framework is designed to identify the key expression, thereby mitigating the potential confusion caused by non-target expressions. RDFER employs two sequence models with the objective of disentangling short-term facial movements and long-term emotional changes. The proposed method has been shown to outperform current State-Of-The-Art approaches in DFER through extensive experimentation on benchmark datasets such as DFEW and FERV39K. A comprehensive analysis provides valuable insights and observations regarding the proposed agreement. This work has significant implications for the field of dynamic facial expression recognition and promotes the further development of the field of noise-consistent robust learning in dynamic facial expression recognition. The code is available from [<uri>https://github.com/Cross-Innovation-Lab/RDFER</uri>].","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"563-572"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480085","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

Domain Invariant Noise-Tolerant Learning for Unsupervised Cross-Domain Person Re-ID 无监督跨域人员重新识别的域不变容噪学习

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-08-08 DOI: 10.1109/TBIOM.2025.3596989

Yuxuan Xiao;Jian Yang;Shanshan Zhang

{"title":"Domain Invariant Noise-Tolerant Learning for Unsupervised Cross-Domain Person Re-ID","authors":"Yuxuan Xiao;Jian Yang;Shanshan Zhang","doi":"10.1109/TBIOM.2025.3596989","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3596989","url":null,"abstract":"In this article, we focus on tackling the task of unsupervised cross-domain adaptation on person re-identification (UDA re-ID). Most previous methods formulate UDA re-ID as a semi-supervised learning problem and aim to generate pseudo-labels on the target domain for training. However, the quality of pseudo-labels is far from satisfactory due to large domain gaps. To address this problem, in this work, we propose to improve the quality of pseudo-labels by handling domain shifts. Specifically, we narrow down the domain gap via domain style normalization and allow the model to behave more robustly to domain shifts by applying different perturbations on the target data. Furthermore, we point out that noises are inevitable in pseudo-labels and propose a noise-tolerant learning paradigm, which consists of positive learning on latent clean pseudo-labels and negative learning on latent noisy pseudo-labels. We conduct experiments under different adaptation scenarios, including synthetic<inline-formula> <tex-math>$rightarrow $ </tex-math></inline-formula>real and real<inline-formula> <tex-math>$rightarrow $ </tex-math></inline-formula>real, and demonstrate that our proposed Domain Invariant Noise-Tolerant Learning (DINTL) method achieves state-of-the-art results without additional parameters.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"770-779"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480573","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

Optimizing Hand Gesture Recognition With an Accurate Graph Model and Mixture of Experts for Joint Relationships and Temporal Dynamics 基于联合关系和时间动态的精确图形模型和混合专家优化手势识别

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-08-05 DOI: 10.1109/TBIOM.2025.3596014

Mohamed Youssef Memmi;Rim Slama;Stefano Berretti

{"title":"Optimizing Hand Gesture Recognition With an Accurate Graph Model and Mixture of Experts for Joint Relationships and Temporal Dynamics","authors":"Mohamed Youssef Memmi;Rim Slama;Stefano Berretti","doi":"10.1109/TBIOM.2025.3596014","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3596014","url":null,"abstract":"Hand gesture recognition approaches have recently achieved promising results by extracting spatial and temporal features. However, existing methods often miss the relationships between distant joints and are hindered by high computational costs. In this article, we introduce a novel graph-based model for hand gesture recognition that overcomes these limitations by efficiently capturing dependencies between distant joints within a unified semantic space and requiring less computation time. Our model consists of three main components: the Mixture of Experts (MoE) framework, which dynamically selects specialized sub-models focused on joints, bones, and motion features to optimize performance and efficiency; the Graph Hierarchical Edge Representation (GHER), which captures multi-scale relationships between joints and models both local and global interactions within the hand skeleton; and the Light Graph Temporal Fusion Transformer (LGTFT), which integrates an attention-based graph transformer with a lightweight Gated Recurrent Unit (GRU) to efficiently capture temporal dynamics, while reducing computational costs. We validate the proposed model through extensive experiments on three benchmark hand gesture recognition datasets: IPN, SHREC’17, and Briareo. Our approach achieves superior results when compared to state-of-the-art methods, while reducing computational time.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"780-793"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480670","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

3-D Face De-Identification With Preserving Multi-Facial Attributes: A Benchmark 保留多人脸属性的三维人脸去识别：一个基准

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-03-15 DOI: 10.1109/TBIOM.2025.3570599

Yang Liu;Kevin H. M. Cheng;Marko Savic;Haoyu Chen;Zitong Yu;Guoying Zhao

{"title":"3-D Face De-Identification With Preserving Multi-Facial Attributes: A Benchmark","authors":"Yang Liu;Kevin H. M. Cheng;Marko Savic;Haoyu Chen;Zitong Yu;Guoying Zhao","doi":"10.1109/TBIOM.2025.3570599","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3570599","url":null,"abstract":"Facial analysis, such as emotion detection from facial data, is inherently vulnerable to privacy due to the rich and sensitive biometric information embedded within facial features. Privacy protection can typically be achieved by modifying face images to alter features associated with facial attributes or biometric identities. However, altering facial attributes often results in the loss of critical information necessary for facial analysis applications, while most existing approaches to modifying biometric identities have not thoroughly evaluated their impact on preserving facial attributes. Additionally, 3D information like depth and surface normal has become increasingly prominent, necessitating privacy protection methods compatible with 3D data. This paper presents a pioneering study on 2D/3D face de-identification that preserves facial attributes, i.e., facial expression, gender, ethnicity. Our method processes face images such that the original identities are unrecognizable to both humans and machines, while retaining essential facial attributes. Systematic and comprehensive experimental results using three publicly available 3D face databases with evaluations on the performance of 2D/3D face/facial attribute recognition demonstrate the effectiveness of our proposed de-identification approach with preserving facial attributes. Implementation codes are also made available to reproduce our experimental results for benchmarking and further research.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"681-694"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11005387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480622","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

Multi-Scale High-Frequency Vision Transformer for Face Forgery Detection 人脸伪造检测的多尺度高频视觉变压器

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-02-19 DOI: 10.1109/TBIOM.2025.3543692

Zhentao Chen;Huimin Li;Junlin Hu

{"title":"Multi-Scale High-Frequency Vision Transformer for Face Forgery Detection","authors":"Zhentao Chen;Huimin Li;Junlin Hu","doi":"10.1109/TBIOM.2025.3543692","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3543692","url":null,"abstract":"In recent years, as more and more mature face forgery methods have been disclosed, the forged face has caused a lot of negative effects in the society, so people hope to develop effective face forgery detection methods to solve this problem. Although a lot of recent work has achieved good performance, there is still room for improvement in the forgery face which is forged by unknown means. Based on the recent observation and analysis, there are two promising ways that may improve the performance of face forgery detection methods, i.e., effectively exploiting the high-frequency features and reducing the interference of identity information. In this paper, we consider both of these aspects and the powerful feature representation capability of Vision Transformer (ViT), and propose a Multi-scale High-frequency Vision Transformer (MH-ViT) method that can effectively use the multi-scale high-frequency features of face images. In order to reduce the interference of identity information to the face forgery detection method as much as possible, this paper randomly partitions a face image into blocks to weaken the identity information, and designs the Patch SoftMax (PSM) loss function. Moreover, to supplement the PSM loss function, we also add a metric learning module and design the Single Center Cosine Similarity (SCCS) loss function with a large margin to expand the distance between real and fake samples. Extensive experiments on both in-domain and cross-domain face forgery detection datasets demonstrate the effectiveness of our proposed MH-ViT method.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"740-750"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480019","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

Unsupervised Gait Recognition With Selective Fusion 基于选择性融合的无监督步态识别

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-02-21 DOI: 10.1109/TBIOM.2025.3544805

Xuqian Ren;Shaopeng Yang;Saihui Hou;Chunshui Cao;Xu Liu;Yongzhen Huang

引用次数: 0

Multi-Directional Shifted Patch Encoding With Transformers for Non-Invasive Cattle Identification 基于变压器的多方向移位贴片编码用于非侵入性牛识别

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-08-15 DOI: 10.1109/TBIOM.2025.3599374

Niraj Kumar;Sanjay Kumar Singh

{"title":"Multi-Directional Shifted Patch Encoding With Transformers for Non-Invasive Cattle Identification","authors":"Niraj Kumar;Sanjay Kumar Singh","doi":"10.1109/TBIOM.2025.3599374","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3599374","url":null,"abstract":"Conventional identification techniques for cattle, such as branding, ear tagging, and notching, though widely implemented, are intrusive and pose scalability limitations in managing large livestock cattle population. While electronic tagging facilitates improved monitoring, it depends on specific devices and is vulnerable to security and maintenance issues. As a non-invasive alternative, biometric identification using unique dermatoglyphic characteristics like muzzle patterns offers a promising direction. Although convolutional neural networks have been applied to extract these biometric features, the use of Vision Transformers remains underexplored due to their dependency on large-scale data, which restricts their effectiveness in real-world agricultural settings with limited datasets. This research presents a transformer-based model incorporating a multi-directional patch tokenization encoding approach involving multiple spatial shifts, coupled with diagonal attention filtering and a global information fusion strategy. This enables the transformer to perform robustly even with constrained training data. The proposed method outperforms traditional Vision Transformer variants, including Swin Transformer and GCViT, and surpasses CNN architectures such as ResNet50 in both accuracy and training efficiency. The final model configuration achieves a peak training accuracy of 98.42% and a validation accuracy of 97.80%. These results demonstrate the viability of transformer-based frameworks, when appropriately adapted, for practical and scalable biometric cattle identification, especially in data-constrained environments.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"620-631"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478701","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

Mitigating Replication and Bias in Diffusion-Based Face Image Synthesis Models With Rewards 带奖励的基于扩散的人脸图像合成模型中的抑制复制和偏差

IF 4.4

IEEE transactions on biometrics, behavior, and identity science Pub Date : 2025-10-01 Epub Date: 2025-07-18 DOI: 10.1109/TBIOM.2025.3590344

Malsha V. Perera;Vishal M. Patel

{"title":"Mitigating Replication and Bias in Diffusion-Based Face Image Synthesis Models With Rewards","authors":"Malsha V. Perera;Vishal M. Patel","doi":"10.1109/TBIOM.2025.3590344","DOIUrl":"https://doi.org/10.1109/TBIOM.2025.3590344","url":null,"abstract":"Diffusion-based image synthesis models have gained a lot of traction in recent years. However, a notable observation is that when trained on smaller datasets, diffusion models tend to replicate training data. This issue is particularly problematic in domains such as medical imaging and biometrics, where data is both limited and sensitive. Moreover, diffusion models have been observed to amplify facial attribute biases. Therefore, addressing these issues is crucial, as they can have adverse effects on the downstream applications utilizing these diffusion models. In this paper, we propose a novel methodology to mitigate both data replication and social biases by using reward maximization to fine-tune pre-trained unconditional diffusion models, aiming to achieve a desired output distribution. Our results show the effectiveness of the proposed method in successfully mitigating gender and racial biases present in pre-trained unconditional diffusion models employed for face generation. Furthermore, our method significantly reduces data replication in unconditional diffusion models, highlighting its potential to enhance the behavior of these generative models.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"7 4","pages":"550-562"},"PeriodicalIF":4.4,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479019","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