Face_Recognition
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DArFace: Deformation Aware Robustness for Low Quality Face Recognition
2025-05-13 10:35:57Sadaf Gulshad, Abdullah Aldahlawi ThakaaAbstract
Facial recognition systems have achieved remarkable success by leveraging deep neural networks, advanced loss functions, and large-scale datasets. However, their performance often deteriorates in real-world scenarios involving low-quality facial images. Such degradations, common in surveillance footage or standoff imaging include low resolution, motion blur, and various distortions, resulting in a substantial domain gap from the high-quality data typically used during training. While existing approaches attempt to address robustness by modifying network architectures or modeling global spatial transformations, they frequently overlook local, non-rigid deformations that are inherently present in real-world settings. In this work, we introduce DArFace, a Deformation-Aware robust Face recognition framework that enhances robustness to such degradations without requiring paired high- and low-quality training samples. Our method adversarially integrates both global transformations (e.g., rotation, translation) and local elastic deformations during training to simulate realistic low-quality conditions. Moreover, we introduce a contrastive objective to enforce identity consistency across different deformed views. Extensive evaluations on low-quality benchmarks including TinyFace, IJB-B, and IJB-C demonstrate that DArFace surpasses state-of-the-art methods, with significant gains attributed to the inclusion of local deformation modeling.
Abstract (translated)
面部识别系统通过利用深度神经网络、先进的损失函数和大规模数据集取得了显著的成功。然而，在涉及低质量面部图像的现实场景中，例如监控录像或远距离成像中的低分辨率、运动模糊以及各种失真，这些系统的性能往往会出现下降。这些问题导致了训练时通常使用的高质量数据与实际应用中存在的低质量数据之间存在着较大的领域差距（domain gap）。 虽然现有方法试图通过修改网络架构或者模拟全局空间变换来提高系统的鲁棒性，但它们常常忽视了在现实环境中普遍存在且固有的局部非刚体变形。为此，在这项工作中，我们引入了一种名为DArFace的“感知变形”的面部识别框架，该框架能够在不使用高质量和低质量配对训练样本的情况下提升系统对抗这些退化的性能。 我们的方法通过在训练过程中模拟全局变换（如旋转、平移）以及局部弹性变形来敌对性地集成两种类型的变形，从而模拟实际的低质量情况。此外，我们引入了一个对比目标以确保不同变形视图之间的身份一致性。 我们在包括TinyFace、IJB-B和IJB-C在内的多个低质量基准数据集上进行了广泛的评估，结果显示DArFace超越了现有的最佳方法，并且显著的性能提升主要归因于局部变形建模的加入。
URL
https://arxiv.org/abs/2505.08423
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Automated Visual Attention Detection using Mobile Eye Tracking in Behavioral Classroom Studies
2025-05-12 13:30:30Efe Bozkir, Christian Kosel, Tina Seidel, Enkelejda KasneciAbstract
Teachers' visual attention and its distribution across the students in classrooms can constitute important implications for student engagement, achievement, and professional teacher training. Despite that, inferring the information about where and which student teachers focus on is not trivial. Mobile eye tracking can provide vital help to solve this issue; however, the use of mobile eye tracking alone requires a significant amount of manual annotations. To address this limitation, we present an automated processing pipeline concept that requires minimal manually annotated data to recognize which student the teachers focus on. To this end, we utilize state-of-the-art face detection models and face recognition feature embeddings to train face recognition models with transfer learning in the classroom context and combine these models with the teachers' gaze from mobile eye trackers. We evaluated our approach with data collected from four different classrooms, and our results show that while it is possible to estimate the visually focused students with reasonable performance in all of our classroom setups, U-shaped and small classrooms led to the best results with accuracies of approximately 0.7 and 0.9, respectively. While we did not evaluate our method for teacher-student interactions and focused on the validity of the technical approach, as our methodology does not require a vast amount of manually annotated data and offers a non-intrusive way of handling teachers' visual attention, it could help improve instructional strategies, enhance classroom management, and provide feedback for professional teacher development.
Abstract (translated)
教师在教室中的视觉注意力及其对学生的分配对于学生参与度、学业成就和专业教师培训具有重要的影响。然而，推断出教师关注的是哪位学生以及他们关注的位置并不容易。移动眼动追踪技术可以为解决这一问题提供重要帮助；然而，仅使用移动眼动追踪需要大量的手动标注数据。为了克服这个限制，我们提出了一种自动化处理流水线的概念，该概念只需要少量的手动标注数据就能识别出教师关注的是哪位学生。 为此，我们利用最先进的面部检测模型和面部识别特征嵌入，在课堂环境中通过迁移学习训练面部识别模型，并将这些模型与来自移动眼动追踪器的教师注视信息结合在一起。我们在四个不同教室收集的数据上评估了我们的方法，结果表明，在所有教室布置中都能合理地估计出视觉关注的学生，而U形和小型教室的表现最佳，准确率分别为大约0.7和0.9。 虽然我们没有评估这种方法在教师与学生互动方面的效果，并且主要验证了技术方法的有效性，但我们的方法不依赖大量手动标注数据，并提供了一种非侵入式的处理教师视觉注意力的方式。因此，它可以帮助改进教学策略、提高课堂管理效率以及为专业教师发展提供反馈。
URL
https://arxiv.org/abs/2505.07552
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FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images
2025-05-12 13:12:33Raul Ismayilov, Luuk Spreeuwers, Dzemila SeroAbstract
Synthetic face datasets are increasingly used to overcome the limitations of real-world biometric data, including privacy concerns, demographic imbalance, and high collection costs. However, many existing methods lack fine-grained control over identity attributes and fail to produce paired, identity-consistent images under structured capture conditions. We introduce FLUXSynID, a framework for generating high-resolution synthetic face datasets with user-defined identity attribute distributions and paired document-style and trusted live capture images. The dataset generated using the FLUXSynID framework shows improved alignment with real-world identity distributions and greater inter-set diversity compared to prior work. The FLUXSynID framework for generating custom datasets, along with a dataset of 14,889 synthetic identities, is publicly released to support biometric research, including face recognition and morphing attack detection.
Abstract (translated)
合成面部数据集的使用日益增加，旨在克服现实世界生物识别数据的限制，包括隐私问题、人口统计失衡以及高昂的数据收集成本。然而，许多现有的方法缺乏对身份属性的精细控制，并且无法在结构化的拍摄条件下生成成对的一致身份图像。我们引入了FLUXSynID框架，用于根据用户定义的身份属性分布生成高分辨率的合成面部数据集，并生成文档样式和可信实时捕捉的配对图像。 使用FLUXSynID框架生成的数据集中，与真实世界中的身份分布的契合度得到了提升，并且在跨集合多样性方面超越了先前的工作。包括14,889个合成身份的数据集以及用于生成定制数据集的FLUXSynID框架已公开发布，以支持生物识别研究，如面部识别和变体攻击检测。
URL
https://arxiv.org/abs/2505.07530
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Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait
2025-05-07 17:58:25Feng Liu, Nicholas Chimitt, Lanqing Guo, Jitesh Jain, Aditya Kane, Minchul Kim, Wes Robbins, Yiyang Su, Dingqiang Ye, Xingguang Zhang, Jie Zhu, Siddharth Satyakam, Christopher Perry, Stanley H. Chan, Arun Ross, Humphrey Shi, Zhangyang Wang, Anil Jain, Xiaoming LiuAbstract
We address the problem of whole-body person recognition in unconstrained environments. This problem arises in surveillance scenarios such as those in the IARPA Biometric Recognition and Identification at Altitude and Range (BRIAR) program, where biometric data is captured at long standoff distances, elevated viewing angles, and under adverse atmospheric conditions (e.g., turbulence and high wind velocity). To this end, we propose FarSight, a unified end-to-end system for person recognition that integrates complementary biometric cues across face, gait, and body shape modalities. FarSight incorporates novel algorithms across four core modules: multi-subject detection and tracking, recognition-aware video restoration, modality-specific biometric feature encoding, and quality-guided multi-modal fusion. These components are designed to work cohesively under degraded image conditions, large pose and scale variations, and cross-domain gaps. Extensive experiments on the BRIAR dataset, one of the most comprehensive benchmarks for long-range, multi-modal biometric recognition, demonstrate the effectiveness of FarSight. Compared to our preliminary system, this system achieves a 34.1% absolute gain in 1:1 verification accuracy (TAR@0.1% FAR), a 17.8% increase in closed-set identification (Rank-20), and a 34.3% reduction in open-set identification errors (FNIR@1% FPIR). Furthermore, FarSight was evaluated in the 2025 NIST RTE Face in Video Evaluation (FIVE), which conducts standardized face recognition testing on the BRIAR dataset. These results establish FarSight as a state-of-the-art solution for operational biometric recognition in challenging real-world conditions.
Abstract (translated)
我们解决了在非受控环境中进行全身人物识别的问题。该问题主要出现在诸如IARPA生物识别和高度范围识别（BRIAR）计划之类的监控场景中，其中生物特征数据是在长距离、高视角以及恶劣的大气条件下（例如湍流和强风速）捕获的。为此，我们提出了FarSight系统，这是一个统一的端到端人物识别系统，该系统整合了面部、步态和体型等多模态生物特征线索。 FarSight包含四个核心模块的新算法：多主体检测与跟踪、以识别为导向的视频恢复、特定于模式的生物特征编码以及质量导向的跨模态融合。这些组件设计为在图像退化条件、大姿态和尺度变化以及跨域差距下协同工作。 通过对BRIAR数据集进行广泛实验，该数据集是用于长距离多模态生物识别基准测试中最全面的数据集之一，证明了FarSight的有效性。与我们的初步系统相比，在一对一验证准确性（TAR@0.1% FAR）方面提高了34.1%，在封闭式身份识别（Rank-20）中提高了17.8%，开放集合身份识别错误减少了34.3%（FNIR@1% FPIR）。此外，FarSight还在NIST 2025年FIVE（BRIAR数据集的标准化视频面部识别测试）中进行了评估。 这些结果使FarSight成为在具有挑战性的现实条件下进行操作生物特征识别的最新解决方案。
URL
https://arxiv.org/abs/2505.04616
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Edge-GPU Based Face Tracking for Face Detection and Recognition Acceleration
2025-05-07 15:57:53Asma Baobaid, Mahmoud MeriboutAbstract
Cost-effective machine vision systems dedicated to real-time and accurate face detection and recognition in public places are crucial for many modern applications. However, despite their high performance, which could be reached using specialized edge or cloud AI hardware accelerators, there is still room for improvement in throughput and power consumption. This paper aims to suggest a combined hardware-software approach that optimizes face detection and recognition systems on one of the latest edge GPUs, namely NVIDIA Jetson AGX Orin. First, it leverages the simultaneous usage of all its hardware engines to improve processing time. This offers an improvement over previous works where these tasks were mainly allocated automatically and exclusively to the CPU or, to a higher extent, to the GPU core. Additionally, the paper suggests integrating a face tracker module to avoid redundantly running the face recognition algorithm for every frame but only when a new face appears in the scene. The results of extended experiments suggest that simultaneous usage of all the hardware engines that are available in the Orin GPU and tracker integration into the pipeline yield an impressive throughput of 290 FPS (frames per second) on 1920 x 1080 input size frames containing in average of 6 faces/frame. Additionally, a substantial saving of power consumption of around 800 mW was achieved when compared to running the task on the CPU/GPU engines only and without integrating a tracker into the Orin GPU\'92s pipeline. This hardware-codesign approach can pave the way to design high-performance machine vision systems at the edge, critically needed in video monitoring in public places where several nearby cameras are usually deployed for a same scene.
Abstract (translated)
高效的机器视觉系统在公共场所进行实时且精确的人脸检测和识别对于许多现代应用至关重要。尽管通过使用专门的边缘或云端AI硬件加速器可以实现高性能，但在吞吐量和功耗方面仍存在改进空间。本文提出了一种结合软硬件的方法，旨在优化基于最新边缘GPU（NVIDIA Jetson AGX Orin）的人脸检测和识别系统。 首先，该方法利用了所有可用的硬件引擎的同时使用来提高处理时间。这比以往只将任务主要分配给CPU或更大程度上专用于GPU核心的做法有所改进。此外，还建议在系统中集成一个面部追踪模块，以避免对每一帧都运行人脸识别算法，而是在新的面孔出现在场景时才进行。 通过广泛的实验结果表明，在Orin GPU上同时使用所有可用的硬件引擎，并将跟踪器整合到管道中，可以实现每秒290帧（FPS）的惊人吞吐量，输入为1920 x 1080尺寸的画面且平均每帧包含6张面孔。此外，与仅在CPU/GPU引擎上运行任务而不集成追踪器相比，在Orin GPU的管道内整合追踪器实现了大约800毫瓦（mW）的功耗节省。 这种软硬件协同设计的方法为边缘高绩效机器视觉系统的开发铺平了道路，这对于公共场所中的视频监控尤为关键，通常需要部署多个近距离摄像头来覆盖同一场景。
URL
https://arxiv.org/abs/2505.04524
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Leveraging Simultaneous Usage of Edge GPU Hardware Engines for Video Face Detection and Recognition
2025-05-07 15:22:17Asma Baobaid, Mahmoud MeriboutAbstract
Video face detection and recognition in public places at the edge is required in several applications, such as security reinforcement and contactless access to authorized venues. This paper aims to maximize the simultaneous usage of hardware engines available in edge GPUs nowadays by leveraging the concurrency and pipelining of tasks required for face detection and recognition. This also includes the video decoding task, which is required in most face monitoring applications as the video streams are usually carried via Gbps Ethernet network. This constitutes an improvement over previous works where the tasks are usually allocated to a single engine due to the lack of a unified and automated framework that simultaneously explores all hardware engines. In addition, previously, the input faces were usually embedded in still images or within raw video streams that overlook the burst delay caused by the decoding stage. The results on real-life video streams suggest that simultaneously using all the hardware engines available in the recent NVIDIA edge Orin GPU, higher throughput, and a slight saving of power consumption of around 300 mW, accounting for around 5%, have been achieved while satisfying the real-time performance constraint. The performance gets even higher by considering several video streams simultaneously. Further performance improvement could have been obtained if the number of shuffle layers that were created by the tensor RT framework for the face recognition task was lower. Thus, the paper suggests some hardware improvements to the existing edge GPU processors to enhance their performance even higher.
Abstract (translated)
在公共场所进行视频面部检测和识别的需求在多个应用中存在，例如加强安全性和实现授权场所的非接触式访问。本文旨在通过利用当前边缘GPU硬件引擎的并发性和流水线作业来最大化这些引擎的同时使用，从而优化面部检测和识别任务。这包括大多数面部监控应用程序所需的视频解码任务，因为视频流通常通过Gbps以太网网络传输。 与以往的工作相比，这项研究有所改进：过去由于缺乏统一且自动化的框架，任务通常被分配给单一硬件引擎进行处理。此外，在之前的实践中，输入的面部通常是嵌入在静态图像或原始视频流中，并未考虑到解码阶段可能造成的突发延迟。实际视频流中的测试结果表明，在最近的NVIDIA边缘Orin GPU上同时使用所有可用硬件引擎能够实现更高的吞吐量和大约300毫瓦（约占5%）的功率节省，这在满足实时性能约束的同时得以体现。此外，考虑到多个视频流时，性能将进一步提高。 进一步的性能改进本可通过减少由TensorRT框架为面部识别任务创建的shuffle层数量来实现。因此，论文建议对现有的边缘GPU处理器进行一些硬件改进以进一步提升其性能。
URL
https://arxiv.org/abs/2505.04502
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MoDE: Mixture of Diffusion Experts for Any Occluded Face Recognition
2025-05-07 10:29:39Qiannan Fan, Zhuoyang Li, Jitong Li, Chenyang CaoAbstract
With the continuous impact of epidemics, people have become accustomed to wearing masks. However, most current occluded face recognition (OFR) algorithms lack prior knowledge of occlusions, resulting in poor performance when dealing with occluded faces of varying types and severity in reality. Recognizing occluded faces is still a significant challenge, which greatly affects the convenience of people's daily lives. In this paper, we propose an identity-gated mixture of diffusion experts (MoDE) for OFR. Each diffusion-based generative expert estimates one possible complete image for occluded faces. Considering the random sampling process of the diffusion model, which introduces inevitable differences and variations between the inpainted faces and the real ones. To ensemble effective information from multi-reconstructed faces, we introduce an identity-gating network to evaluate the contribution of each reconstructed face to the identity and adaptively integrate the predictions in the decision space. Moreover, our MoDE is a plug-and-play module for most existing face recognition models. Extensive experiments on three public face datasets and two datasets in the wild validate our advanced performance for various occlusions in comparison with the competing methods.
Abstract (translated)
随着疫情的持续影响，人们已经习惯了佩戴口罩。然而，大多数当前的遮挡面部识别（OFR）算法缺乏对不同类型和程度遮挡情况的先验知识，导致在处理实际生活中各种类型的遮挡面部时表现不佳。因此，识别人脸中的遮挡部分仍然是一个重大挑战，并且严重影响了人们日常生活的便利性。 本文中，我们提出了一种用于OFR的身份门控扩散专家混合模型（MoDE）。每个基于扩散生成的专家都会估算出一张可能完整的未被遮挡的脸部图像。考虑到扩散模型中的随机采样过程会引入修补后的脸部与真实脸部之间的不可避免差异和变化，为了从多个重建的脸部中集成有效的信息，我们引入了一个身份门控网络来评估每个重建脸部对身份识别的重要性，并根据在决策空间中的预测结果进行自适应整合。 此外，我们的MoDE模块可以作为插件方便地应用于大多数现有的面部识别模型。我们在三个公开的人脸数据集和两个野外环境中收集的数据集上进行了广泛的实验，验证了与竞争方法相比，在处理各种遮挡情况时我们模型的先进性能。
URL
https://arxiv.org/abs/2505.04306
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Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images
2025-05-06 15:09:37Fangling Jiang, Qi Li, Weining Wang, Wei Shen, Bing Liu, Zhenan SunAbstract
Face anti-spoofing is a critical technology for ensuring the security of face recognition systems. However, its ability to generalize across diverse scenarios remains a significant challenge. In this paper, we attribute the limited generalization ability to two key factors: covariate shift, which arises from external data collection variations, and semantic shift, which results from substantial differences in emerging attack types. To address both challenges, we propose a novel approach for learning unknown spoof prompts, relying solely on real face images from a single source domain. Our method generates textual prompts for real faces and potential unknown spoof attacks by leveraging the general knowledge embedded in vision-language models, thereby enhancing the model's ability to generalize to unseen target domains. Specifically, we introduce a diverse spoof prompt optimization framework to learn effective prompts. This framework constrains unknown spoof prompts within a relaxed prior knowledge space while maximizing their distance from real face images. Moreover, it enforces semantic independence among different spoof prompts to capture a broad range of spoof patterns. Experimental results on nine datasets demonstrate that the learned prompts effectively transfer the knowledge of vision-language models, enabling state-of-the-art generalization ability against diverse unknown attack types across unseen target domains without using any spoof face images.
Abstract (translated)
人脸防伪技术是确保人脸识别系统安全性的关键。然而，其在不同场景下的泛化能力仍然是一个重大挑战。在这篇论文中，我们将有限的泛化能力归因于两个重要因素：协变量变化（covariate shift），这是由于外部数据采集差异引起的；以及语义变化（semantic shift），这是由新兴攻击类型的重大区别所导致的。 为了解决这两个挑战，我们提出了一种新的方法来学习未知的防伪提示，仅依赖单个源域的真实人脸图像。我们的方法通过利用视觉-语言模型中嵌入的一般知识生成真实面部和潜在未知伪造攻击的文字提示，从而提高模型在未见目标领域中的泛化能力。 具体来说，我们引入了一个多样化的伪造提示优化框架来学习有效的提示。此框架将未知的伪造提示约束在一个放松的先验知识空间内，同时最大化其与真实人脸图像的距离。此外，它还强制不同的伪造提示之间保持语义独立性，以捕捉广泛的伪造模式。 在九个数据集上的实验结果表明，所学得的提示有效地转移了视觉-语言模型的知识，在没有使用任何伪造面部图像的情况下，能够使模型对未见目标领域中各种未知攻击类型具有最先进的泛化能力。
URL
https://arxiv.org/abs/2505.03611
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Learning Knowledge-based Prompts for Robust 3D Mask Presentation Attack Detection
2025-05-06 15:09:23Fangling Jiang, Qi Li, Bing Liu, Weining Wang, Caifeng Shan, Zhenan Sun, Ming-Hsuan YangAbstract
3D mask presentation attack detection is crucial for protecting face recognition systems against the rising threat of 3D mask attacks. While most existing methods utilize multimodal features or remote photoplethysmography (rPPG) signals to distinguish between real faces and 3D masks, they face significant challenges, such as the high costs associated with multimodal sensors and limited generalization ability. Detection-related text descriptions offer concise, universal information and are cost-effective to obtain. However, the potential of vision-language multimodal features for 3D mask presentation attack detection remains unexplored. In this paper, we propose a novel knowledge-based prompt learning framework to explore the strong generalization capability of vision-language models for 3D mask presentation attack detection. Specifically, our approach incorporates entities and triples from knowledge graphs into the prompt learning process, generating fine-grained, task-specific explicit prompts that effectively harness the knowledge embedded in pre-trained vision-language models. Furthermore, considering different input images may emphasize distinct knowledge graph elements, we introduce a visual-specific knowledge filter based on an attention mechanism to refine relevant elements according to the visual context. Additionally, we leverage causal graph theory insights into the prompt learning process to further enhance the generalization ability of our method. During training, a spurious correlation elimination paradigm is employed, which removes category-irrelevant local image patches using guidance from knowledge-based text features, fostering the learning of generalized causal prompts that align with category-relevant local patches. Experimental results demonstrate that the proposed method achieves state-of-the-art intra- and cross-scenario detection performance on benchmark datasets.
Abstract (translated)
3D面具呈现攻击检测对于保护面部识别系统免受日益增长的3D面具攻击威胁至关重要。尽管大多数现有的方法利用多模态特征或远程光体积描记图（rPPG）信号来区分真实人脸和3D面具，但它们面临着高昂的成本问题以及有限的泛化能力等重大挑战。与检测相关的文本描述提供了简洁、通用的信息，并且易于获取。然而，视觉-语言多模态特性在3D面具呈现攻击检测中的潜力尚未被探索。 本文提出了一种基于知识提示学习的新框架，以挖掘预训练视觉-语言模型的强大泛化能力来进行3D面具呈现攻击的检测。具体而言，我们的方法将知识图谱中的实体和三元组纳入到提示学习过程中，生成细粒度、任务特定的显式提示，从而有效地利用了嵌入在预先训练好的视觉-语言模型中的知识。 此外，考虑到不同输入图像可能强调不同的知识图谱元素，我们引入了一个基于注意力机制的视觉特异性知识过滤器来根据视觉上下文细化相关元素。另外，我们还借鉴因果图理论的思想进一步提升了方法的泛化能力，在训练过程中采用了虚假关联消除范式，该范式通过知识驱动的文本特征指导移除类别不相关的局部图像补丁，促进学习到与类别相关局部补丁相一致的一般性原因提示。 实验结果表明，所提出的方法在基准数据集上的内场景和跨场景检测性能上达到了最先进的水平。
URL
https://arxiv.org/abs/2505.03610
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Attention-aggregated Attack for Boosting the Transferability of Facial Adversarial Examples
2025-05-06 10:02:56Jian-Wei Li, Wen-Ze ShaoAbstract
Adversarial examples have revealed the vulnerability of deep learning models and raised serious concerns about information security. The transfer-based attack is a hot topic in black-box attacks that are practical to real-world scenarios where the training datasets, parameters, and structure of the target model are unknown to the attacker. However, few methods consider the particularity of class-specific deep models for fine-grained vision tasks, such as face recognition (FR), giving rise to unsatisfactory attacking performance. In this work, we first investigate what in a face exactly contributes to the embedding learning of FR models and find that both decisive and auxiliary facial features are specific to each FR model, which is quite different from the biological mechanism of human visual system. Accordingly we then propose a novel attack method named Attention-aggregated Attack (AAA) to enhance the transferability of adversarial examples against FR, which is inspired by the attention divergence and aims to destroy the facial features that are critical for the decision-making of other FR models by imitating their attentions on the clean face images. Extensive experiments conducted on various FR models validate the superiority and robust effectiveness of the proposed method over existing methods.
Abstract (translated)
对抗性样本揭示了深度学习模型的脆弱性，并对信息安全提出了严重关切。基于转移攻击是针对实际场景中黑盒攻击的一个热门话题，在这些场景下，目标模型的训练数据集、参数和结构都是未知的。然而，很少有方法考虑特定于类别的深层模型在细粒度视觉任务（如人脸识别(FR)）中的特性，从而导致了不理想的攻击性能。 在这项工作中，我们首先探讨了面部哪些特征对FR模型的嵌入学习至关重要，并发现决定性和辅助性的面部特征对于每个FR模型都是特有的，这与人类视觉系统的生物机制有着显著的不同。因此，我们提出了一种名为注意力聚合攻击（Attention-aggregated Attack, AAA）的新方法，以增强对抗性样本在针对人脸识别时的转移能力。该方法受注意力分歧启发，旨在通过模仿清洁面部图像上其他FR模型的关注点来破坏对决策至关重要的面部特征。 我们在各种FR模型上进行了广泛的实验，验证了所提出的方法相对于现有方法的优势及其鲁棒有效性。
URL
https://arxiv.org/abs/2505.03383
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GIF: Generative Inspiration for Face Recognition at Scale
2025-05-05 20:23:14Saeed Ebrahimi, Sahar Rahimi, Ali Dabouei, Srinjoy Das, Jeremy M. Dawson, Nasser M. NasrabadiAbstract
Aiming to reduce the computational cost of Softmax in massive label space of Face Recognition (FR) benchmarks, recent studies estimate the output using a subset of identities. Although promising, the association between the computation cost and the number of identities in the dataset remains linear only with a reduced ratio. A shared characteristic among available FR methods is the employment of atomic scalar labels during training. Consequently, the input to label matching is through a dot product between the feature vector of the input and the Softmax centroids. Inspired by generative modeling, we present a simple yet effective method that substitutes scalar labels with structured identity code, i.e., a sequence of integers. Specifically, we propose a tokenization scheme that transforms atomic scalar labels into structured identity codes. Then, we train an FR backbone to predict the code for each input instead of its scalar label. As a result, the associated computational cost becomes logarithmic w.r.t. number of identities. We demonstrate the benefits of the proposed method by conducting experiments. In particular, our method outperforms its competitors by 1.52%, and 0.6% at TAR@FAR$=1e-4$ on IJB-B and IJB-C, respectively, while transforming the association between computational cost and the number of identities from linear to logarithmic. See code at this https URL
Abstract (translated)
为了减少大规模标签空间中人脸识别（FR）基准测试中的Softmax计算成本，最近的研究通过使用身份子集来估计输出。尽管这种方法有前景，但其计算成本与数据集中身份数量之间的关系仍然是线性的，只是比例有所降低。现有可用的FR方法的一个共同特点是，在训练过程中采用原子标量标签。因此，输入和标签匹配是通过输入特征向量与Softmax中心点之间的点积实现的。 受到生成模型的启发，我们提出了一种简单而有效的方法，即用结构化的身份代码替换标量标签，也就是一串整数。具体来说，我们提出了一个标记化方案，将原子标量标签转换为结构化的身份代码。然后，训练FR骨干网络来预测每个输入的身份代码，而不是其标量标签。结果是与身份数量相关的计算成本变为对数关系。 通过实验展示了所提方法的益处：在IJB-B和IJB-C基准测试中，在FAR$=1e-4$下的TAR上，我们的方法分别优于竞争对手1.52%和0.6%，同时将计算成本与身份数量的关系从线性转变为对数。更多详情请参见代码链接：[此处提供实际的URL或指示读者查看原始文档中的具体网址]。 注意：在上述翻译中，“TAR@FAR”是True Acceptance Rate at False Alarm Rate（假警报率下的真实接受率）的缩写，在人脸识别研究领域常用来评估模型性能。
URL
https://arxiv.org/abs/2505.03012
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Explainable Face Recognition via Improved Localization
2025-05-04 21:58:16Rashik Shadman, Daqing Hou, Faraz Hussain, M G Sarwar MurshedAbstract
Biometric authentication has become one of the most widely used tools in the current technological era to authenticate users and to distinguish between genuine users and imposters. Face is the most common form of biometric modality that has proven effective. Deep learning-based face recognition systems are now commonly used across different domains. However, these systems usually operate like black-box models that do not provide necessary explanations or justifications for their decisions. This is a major disadvantage because users cannot trust such artificial intelligence-based biometric systems and may not feel comfortable using them when clear explanations or justifications are not provided. This paper addresses this problem by applying an efficient method for explainable face recognition systems. We use a Class Activation Mapping (CAM)-based discriminative localization (very narrow/specific localization) technique called Scaled Directed Divergence (SDD) to visually explain the results of deep learning-based face recognition systems. We perform fine localization of the face features relevant to the deep learning model for its prediction/decision. Our experiments show that the SDD Class Activation Map (CAM) highlights the relevant face features very specifically compared to the traditional CAM and very accurately. The provided visual explanations with narrow localization of relevant features can ensure much-needed transparency and trust for deep learning-based face recognition systems.
Abstract (translated)
生物特征认证已成为当前技术时代中最广泛使用的工具之一，用于验证用户身份并区分真实用户和冒充者。面部是最常见的生物识别模式，已被证明非常有效。基于深度学习的面部识别系统如今在不同领域中被广泛应用。然而，这些系统通常像黑箱模型一样运作，无法提供决策背后的必要解释或理由。这是一大劣势，因为用户无法信任此类基于人工智能的生物识别系统，并且当没有提供清晰的解释时可能会感到不舒适使用它们。本文通过应用一种用于可解释面部识别系统的高效方法来解决这个问题。我们采用了一种名为尺度定向分歧（Scaled Directed Divergence, SDD）的技术，这是一种基于类激活映射（Class Activation Mapping, CAM）的区分性定位技术，用于对基于深度学习的面部识别系统的结果进行视觉说明。我们在实验中进行了精细定位，确定了与深度学习模型预测/决策相关的面部特征。我们的实验证明，SDD 类激活图比传统的CAM更具体地突出了相关面部特征，并且准确性更高。所提供的具有狭窄局部化特性的可视化解释可以确保基于深度学习的面部识别系统的所需透明度和信任度。
URL
https://arxiv.org/abs/2505.03837
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Transforming faces into video stories -- VideoFace2.0
2025-05-04 10:36:58Branko Brklja\v{c}, Vladimir Kalu\v{s}ev, Branislav Popovi\'c, Milan Se\v{c}ujskiAbstract
Face detection and face recognition have been in the focus of vision community since the very beginnings. Inspired by the success of the original Videoface digitizer, a pioneering device that allowed users to capture video signals from any source, we have designed an advanced video analytics tool to efficiently create structured video stories, i.e. identity-based information catalogs. VideoFace2.0 is the name of the developed system for spatial and temporal localization of each unique face in the input video, i.e. face re-identification (ReID), which also allows their cataloging, characterization and creation of structured video outputs for later downstream tasks. Developed near real-time solution is primarily designed to be utilized in application scenarios involving TV production, media analysis, and as an efficient tool for creating large video datasets necessary for training machine learning (ML) models in challenging vision tasks such as lip reading and multimodal speech recognition. Conducted experiments confirm applicability of the proposed face ReID algorithm that is combining the concepts of face detection, face recognition and passive tracking-by-detection in order to achieve robust and efficient face ReID. The system is envisioned as a compact and modular extensions of the existing video production equipment. We hope that the presented work and shared code will stimulate further interest in development of similar, application specific video analysis tools, and lower the entry barrier for production of high-quality multi-modal ML datasets in the future.
Abstract (translated)
自从视觉社区的早期开始，面部检测和面部识别就一直是研究的重点。受到原始Videoface数字仪成功的启发——这是第一个允许用户从任何来源捕获视频信号的开创性设备——我们设计了一种先进的视频分析工具，可以高效地创建基于身份的信息目录，即结构化视频故事。VideoFace2.0是开发出的一套系统，它能够在输入视频中对每个独特的面部进行空间和时间上的定位，也就是所谓的“重新识别”（ReID），同时允许它们被分类、特征描述，并生成用于后续任务的结构化视频输出。这套近实时解决方案主要设计为在涉及电视制作、媒体分析的应用场景中使用，同时也是为了创建大型视频数据集而设计的一种高效工具，这些数据集对于训练机器学习(ML)模型至关重要，尤其是在唇读和多模态语音识别等具有挑战性的视觉任务上。 进行的实验证实了所提出的面部ReID算法的有效性，该算法结合了面部检测、面部识别以及被动追踪的概念以实现稳健且高效的面部重新识别。该系统构想为现有视频制作设备的一个紧凑而模块化的扩展部分。我们希望这项工作和共享的代码能够激发更多对开发类似的应用特定视频分析工具的兴趣，并在未来降低高质量多模态ML数据集生产的门槛。
URL
https://arxiv.org/abs/2505.02060
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Component-Based Fairness in Face Attribute Classification with Bayesian Network-informed Meta Learning
2025-05-03 05:26:29Yifan Liu, Ruichen Yao, Yaokun Liu, Ruohan Zong, Zelin Li, Yang Zhang, Dong WangAbstract
The widespread integration of face recognition technologies into various applications (e.g., access control and personalized advertising) necessitates a critical emphasis on fairness. While previous efforts have focused on demographic fairness, the fairness of individual biological face components remains unexplored. In this paper, we focus on face component fairness, a fairness notion defined by biological face features. To our best knowledge, our work is the first work to mitigate bias of face attribute prediction at the biological feature level. In this work, we identify two key challenges in optimizing face component fairness: attribute label scarcity and attribute inter-dependencies, both of which limit the effectiveness of bias mitigation from previous approaches. To address these issues, we propose \textbf{B}ayesian \textbf{N}etwork-informed \textbf{M}eta \textbf{R}eweighting (BNMR), which incorporates a Bayesian Network calibrator to guide an adaptive meta-learning-based sample reweighting process. During the training process of our approach, the Bayesian Network calibrator dynamically tracks model bias and encodes prior probabilities for face component attributes to overcome the above challenges. To demonstrate the efficacy of our approach, we conduct extensive experiments on a large-scale real-world human face dataset. Our results show that BNMR is able to consistently outperform recent face bias mitigation baselines. Moreover, our results suggest a positive impact of face component fairness on the commonly considered demographic fairness (e.g., \textit{gender}). Our findings pave the way for new research avenues on face component fairness, suggesting that face component fairness could serve as a potential surrogate objective for demographic fairness. The code for our work is publicly available~\footnote{this https URL}.
Abstract (translated)
面部识别技术在各种应用（例如访问控制和个人化广告）中的广泛应用，要求对公平性给予高度重视。尽管以前的努力主要集中在人口统计学的公平性上，但关于个体生物面部组件的公平性仍未被探索。在这篇文章中，我们专注于面部组件公平性，这是一种由生物学面部特征定义的公平性概念。据我们所知，我们的工作是首次在生物学特征水平上减轻面部属性预测偏差的研究。 在此工作中，我们确定了优化面部组件公平性的两个关键挑战：属性标签稀缺性和属性间的相互依赖关系，这些都限制了以前方法消除偏见的有效性。为了解决这些问题，我们提出了基于贝叶斯网络的元重加权（BNMR）方法，该方法引入了一个贝叶斯网络校准器来引导一个自适应的元学习样本重新加权过程。在我们的方法训练过程中，贝叶斯网络校准器会动态跟踪模型偏差，并为面部组件属性编码先验概率以克服上述挑战。 为了展示我们方法的有效性，我们在大规模的真实世界人类面部数据集上进行了广泛的实验。结果显示，BNMR能够持续优于最近的面部偏见缓解基线。此外，我们的结果表明，面部组件公平性对通常考虑的人口统计学公平性（例如性别）具有积极影响。我们的发现为面部组件公平性的新研究方向铺平了道路，暗示面部组件公平性可以作为人口统计学公平性的潜在替代目标。我们工作的代码公开可用\footnote{this https URL}。
URL
https://arxiv.org/abs/2505.01699
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The Invisible Threat: Evaluating the Vulnerability of Cross-Spectral Face Recognition to Presentation Attacks
2025-05-01 08:15:48Anjith George, Sebastien MarcelAbstract
Cross-spectral face recognition systems are designed to enhance the performance of facial recognition systems by enabling cross-modal matching under challenging operational conditions. A particularly relevant application is the matching of near-infrared (NIR) images to visible-spectrum (VIS) images, enabling the verification of individuals by comparing NIR facial captures acquired with VIS reference images. The use of NIR imaging offers several advantages, including greater robustness to illumination variations, better visibility through glasses and glare, and greater resistance to presentation attacks. Despite these claimed benefits, the robustness of NIR-based systems against presentation attacks has not been systematically studied in the literature. In this work, we conduct a comprehensive evaluation into the vulnerability of NIR-VIS cross-spectral face recognition systems to presentation attacks. Our empirical findings indicate that, although these systems exhibit a certain degree of reliability, they remain vulnerable to specific attacks, emphasizing the need for further research in this area.
Abstract (translated)
跨光谱人脸识别系统旨在通过启用在挑战性的操作条件下进行跨模态匹配，来增强面部识别系统的性能。一个特别相关的应用是将近红外（NIR）图像与可见光谱（VIS）图像进行匹配，使得可以通过比较从可见光谱参考图像中获取的近红外面部捕捉图像来验证个人身份。使用近红外成像提供了一些优势，包括更强的光照变化鲁棒性、更好的透过眼镜和眩光效果以及对呈现攻击更强的抵抗力。尽管这些系统声称具有诸多优点，但基于近红外的系统在面对呈现攻击时的鲁棒性的研究尚未在文献中得到系统的探讨。 在这项工作中，我们进行了全面评估跨光谱（NIR-VIS）人脸识别系统对于呈现攻击的脆弱性。我们的实证研究表明，虽然这些系统显示了一定程度上的可靠性，但它们仍然容易受到特定类型的攻击，这强调了在此领域进一步研究的需求。
URL
https://arxiv.org/abs/2505.00380
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Diffusion-based Adversarial Identity Manipulation for Facial Privacy Protection
2025-04-30 13:49:59Liqin Wang, Qianyue Hu, Wei Lu, Xiangyang LuoAbstract
The success of face recognition (FR) systems has led to serious privacy concerns due to potential unauthorized surveillance and user tracking on social networks. Existing methods for enhancing privacy fail to generate natural face images that can protect facial privacy. In this paper, we propose diffusion-based adversarial identity manipulation (DiffAIM) to generate natural and highly transferable adversarial faces against malicious FR systems. To be specific, we manipulate facial identity within the low-dimensional latent space of a diffusion model. This involves iteratively injecting gradient-based adversarial identity guidance during the reverse diffusion process, progressively steering the generation toward the desired adversarial faces. The guidance is optimized for identity convergence towards a target while promoting semantic divergence from the source, facilitating effective impersonation while maintaining visual naturalness. We further incorporate structure-preserving regularization to preserve facial structure consistency during manipulation. Extensive experiments on both face verification and identification tasks demonstrate that compared with the state-of-the-art, DiffAIM achieves stronger black-box attack transferability while maintaining superior visual quality. We also demonstrate the effectiveness of the proposed approach for commercial FR APIs, including Face++ and Aliyun.
Abstract (translated)
面部识别（FR）系统的成功引发了严重的隐私担忧，因为它们可能被未经授权地用于监视和跟踪社交媒体上的用户。现有的增强隐私的方法无法生成能够保护面部隐私的自然人脸图像。在本文中，我们提出了一种基于扩散的对抗性身份操纵方法（DiffAIM），旨在针对恶意面部识别系统生成自然且高度可转移的对抗性面孔。具体而言，我们在扩散模型的低维潜在空间内操控面部身份，并通过反向扩散过程中迭代注入梯度导向的对抗身份引导，逐步将生成过程引导到所需的对抗性面孔上。这种引导优化了身份朝目标收敛的同时促进了语义与来源图像的差异，从而在保持视觉自然性的前提下有效进行伪装。我们还引入了一种结构保留正则化方法，在操作过程中保持面部结构的一致性。在人脸验证和识别任务上的广泛实验表明，与现有最佳技术相比，DiffAIM能够在维持更优质的视觉质量的同时实现更强的黑盒攻击转移能力。此外，我们展示了该方法对商业面部识别API（如Face++和阿里云）的有效性。
URL
https://arxiv.org/abs/2504.21646
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xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices
2025-04-28 10:03:11Anjith George, Sebastien MarcelAbstract
Heterogeneous Face Recognition (HFR) addresses the challenge of matching face images across different sensing modalities, such as thermal to visible or near-infrared to visible, expanding the applicability of face recognition systems in real-world, unconstrained environments. While recent HFR methods have shown promising results, many rely on computation-intensive architectures, limiting their practicality for deployment on resource-constrained edge devices. In this work, we present a lightweight yet effective HFR framework by adapting a hybrid CNN-Transformer architecture originally designed for face recognition. Our approach enables efficient end-to-end training with minimal paired heterogeneous data while preserving strong performance on standard RGB face recognition tasks. This makes it a compelling solution for both homogeneous and heterogeneous scenarios. Extensive experiments across multiple challenging HFR and face recognition benchmarks demonstrate that our method consistently outperforms state-of-the-art approaches while maintaining a low computational overhead.
Abstract (translated)
异构人脸识别（Heterogeneous Face Recognition, HFR）旨在解决跨不同传感模式匹配面部图像的挑战，如从热成像到可见光或近红外到可见光的情况，从而扩展了面部识别系统在现实世界中无约束环境中的适用性。尽管最近的一些HFR方法展示了令人鼓舞的结果，但许多这些方法依赖于计算密集型架构，这限制了它们在资源受限的边缘设备上的实用性。在这项工作中，我们提出了一种轻量级且有效的HFR框架，通过适应一种最初为面部识别设计的混合CNN-Transformer架构来实现这一目标。我们的方法能够在使用少量配对异构数据的情况下进行高效的端到端训练，并保持强大的标准RGB面部识别任务性能。这使其成为解决同质和异质场景问题的一个引人注目的解决方案。在多个具有挑战性的HFR和面部识别基准测试上的广泛实验表明，我们的方法始终优于现有最佳方法，同时保持较低的计算开销。
URL
https://arxiv.org/abs/2504.19646
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Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance
2025-04-26 10:21:46Simone Maurizio La Cava, Roberto Casula, Sara Concas, Giulia Orr\`u, Ruben Tolosana, Martin Drahansky, Julian Fierrez, Gian Luca MarcialisAbstract
3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to the limits and characteristics of the different application scenarios. In this study, we investigate how multiple state-of-the-art 3DFR algorithms can be used to generate a better representation of subjects, with the final goal of improving the performance of face recognition systems in challenging uncontrolled scenarios. We also explore how different parametric and non-parametric score-level fusion methods can exploit the unique strengths of multiple 3DFR algorithms to enhance biometric recognition robustness. With this goal, we propose a comprehensive analysis of several face recognition systems across diverse conditions, such as varying distances and camera setups, intra-dataset and cross-dataset, to assess the robustness of the proposed ensemble method. The results demonstrate that the distinct information provided by different 3DFR algorithms can alleviate the problem of generalizing over multiple application scenarios. In addition, the present study highlights the potential of advanced fusion strategies to enhance the reliability of 3DFR-based face recognition systems, providing the research community with key insights to exploit them in real-world applications effectively. Although the experiments are carried out in a specific face verification setup, our proposed fusion-based 3DFR methods may be applied to other tasks around face biometrics that are not strictly related to identity recognition.
Abstract (translated)
基于特定假设的3D人脸重建（3DFR）算法适用于不同应用场景的局限性和特点。在这项研究中，我们探讨了如何利用多种最先进的3DFR算法来生成更好的人物表示，以最终提高在具有挑战性的非受控环境下的面部识别系统的性能。此外，我们也探索了不同的参数化和非参数化的评分级融合方法如何利用多个3DFR算法的独特优势来增强生物特征识别的鲁棒性。为了达到这个目标，我们提出了一种对多种人脸识别系统进行全面分析的方法，在各种条件下进行测试，包括不同距离、摄像机设置以及跨数据集等，以评估所提出的集成方法的鲁棒性。研究结果表明，不同的3DFR算法所提供的独特信息可以缓解在多个应用场景中泛化的问题。此外，本研究还强调了高级融合策略增强基于3DFR的人脸识别系统可靠性的潜力，并为研究社区提供了有效利用这些策略的关键见解，以应用于现实世界的应用场景中。尽管实验是在特定的面部验证设置中进行的，但我们的提出的基于融合的3DFR方法可以应用于其他与身份识别不严格相关的面部生物特征任务上。
URL
https://arxiv.org/abs/2504.18886
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Diffusion-Driven Universal Model Inversion Attack for Face Recognition
2025-04-25 01:53:27Hanrui Wang, Shuo Wang, Chun-Shien Lu, Isao EchizenAbstract
Facial recognition technology poses significant privacy risks, as it relies on biometric data that is inherently sensitive and immutable if compromised. To mitigate these concerns, face recognition systems convert raw images into embeddings, traditionally considered privacy-preserving. However, model inversion attacks pose a significant privacy threat by reconstructing these private facial images, making them a crucial tool for evaluating the privacy risks of face recognition systems. Existing methods usually require training individual generators for each target model, a computationally expensive process. In this paper, we propose DiffUMI, a training-free diffusion-driven universal model inversion attack for face recognition systems. DiffUMI is the first approach to apply a diffusion model for unconditional image generation in model inversion. Unlike other methods, DiffUMI is universal, eliminating the need for training target-specific generators. It operates within a fixed framework and pretrained diffusion model while seamlessly adapting to diverse target identities and models. DiffUMI breaches privacy-preserving face recognition systems with state-of-the-art success, demonstrating that an unconditional diffusion model, coupled with optimized adversarial search, enables efficient and high-fidelity facial reconstruction. Additionally, we introduce a novel application of out-of-domain detection (OODD), marking the first use of model inversion to distinguish non-face inputs from face inputs based solely on embeddings.
Abstract (translated)
面部识别技术带来了重大的隐私风险，因为它依赖于敏感且一旦泄露便无法更改的生物识别数据。为了减轻这些担忧，面部识别系统将原始图像转换为嵌入（embeddings），这通常被认为可以保护隐私。然而，模型逆向攻击通过重建这些私人的面部图像构成了一个重要的隐私威胁，并成为评估面部识别系统的隐私风险的关键工具。现有的方法通常需要针对每个目标模型训练专用的生成器，这是一个计算成本高昂的过程。 本文提出了一种名为DiffUMI的新技术，这是一种无需训练、基于扩散驱动的通用模型逆向攻击方法，专门用于面部识别系统。DiffUMI是首个在模型逆向过程中应用无条件图像生成扩散模型的方法。与其它方法不同的是，DiffUMI具备普适性，不需要针对特定目标进行训练，而是在固定的框架和预训练的扩散模型内运行，并能轻松适应各种不同的目标身份及模型。 DiffUMI能够有效破解隐私保护型面部识别系统，展示了无条件扩散模型结合优化后的对抗搜索技术可以实现高效且高保真度的人脸图像重建。此外，本文还提出了一种新颖的应用——领域外检测（OODD），这是首次利用模型逆向攻击仅凭嵌入来区分非人脸输入和人脸输入的方法。 总的来说，这项研究展示了如何使用先进的机器学习方法来评估并提升面部识别系统的隐私保护能力，并为未来的研究提供了新的方向。
URL
https://arxiv.org/abs/2504.18015
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Human-Imperceptible Physical Adversarial Attack for NIR Face Recognition Models
2025-04-22 12:10:25Songyan Xie, Jinghang Wen, Encheng Su, Qiucheng YuAbstract
Near-infrared (NIR) face recognition systems, which can operate effectively in low-light conditions or in the presence of makeup, exhibit vulnerabilities when subjected to physical adversarial attacks. To further demonstrate the potential risks in real-world applications, we design a novel, stealthy, and practical adversarial patch to attack NIR face recognition systems in a black-box setting. We achieved this by utilizing human-imperceptible infrared-absorbing ink to generate multiple patches with digitally optimized shapes and positions for infrared images. To address the optimization mismatch between digital and real-world NIR imaging, we develop a light reflection model for human skin to minimize pixel-level discrepancies by simulating NIR light reflection. Compared to state-of-the-art (SOTA) physical attacks on NIR face recognition systems, the experimental results show that our method improves the attack success rate in both digital and physical domains, particularly maintaining effectiveness across various face postures. Notably, the proposed approach outperforms SOTA methods, achieving an average attack success rate of 82.46% in the physical domain across different models, compared to 64.18% for existing methods. The artifact is available at this https URL.
Abstract (translated)
近红外（NIR）人脸识别系统能够在低光条件或存在化妆的情况下有效运行，但在遭受物理对抗性攻击时表现出脆弱性。为了进一步展示在实际应用中可能存在的风险，我们设计了一种新颖的、隐蔽且实用的对抗贴纸，在黑盒设置下用于攻击NIR人脸识别系统。我们通过使用人类无法察觉的红外吸收墨水来生成多个具有数字优化形状和位置的贴纸，专门针对红外图像进行设计。 为了解决数字与现实世界中NIR成像之间的优化不匹配问题，我们开发了一个光反射模型来模拟近红外光线在人体皮肤上的反射，并通过最小化像素级差异来提高效果。相比于目前最先进的（SOTA）物理攻击方法，实验结果表明我们的方法在数字和实际领域都提高了攻击成功率，特别是在各种面部姿态下仍保持有效性。 值得注意的是，所提出的方法优于现有的SOTA方法，在不同模型的实际环境中实现了82.46%的平均攻击成功率，而现有方法仅为64.18%。相关成果可以在以下链接中找到：[此URL]。
URL
https://arxiv.org/abs/2504.15823
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