Face_Recognition
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Face Identity Unlearning for Retrieval via Embedding Dispersion
2025-12-15 13:35:28Mikhail ZakharovAbstract
Face recognition systems rely on learning highly discriminative and compact identity clusters to enable accurate retrieval. However, as with other surveillance-oriented technologies, such systems raise serious privacy concerns due to their potential for unauthorized identity tracking. While several works have explored machine unlearning as a means of privacy protection, their applicability to face retrieval - especially for modern embedding-based recognition models - remains largely unexplored. In this work, we study the problem of face identity unlearning for retrieval systems and present its inherent challenges. The goal is to make selected identities unretrievable by dispersing their embeddings on the hypersphere and preventing the formation of compact identity clusters that enable re-identification in the gallery. The primary challenge is to achieve this forgetting effect while preserving the discriminative structure of the embedding space and the retrieval performance of the model for the remaining identities. To address this, we evaluate several existing approximate class unlearning methods (e.g., Random Labeling, Gradient Ascent, Boundary Unlearning, and other recent approaches) in the context of face retrieval and propose a simple yet effective dispersion-based unlearning approach. Extensive experiments on standard benchmarks (VGGFace2, CelebA) demonstrate that our method achieves superior forgetting behavior while preserving retrieval utility.
Abstract (translated)
面部识别系统依赖于学习高度判别性和紧凑的身份聚类，以实现准确的检索。然而，与其他监控技术一样，这类系统由于其潜在的未经授权的身份追踪能力而引发了严重的隐私问题。尽管已有几项研究探索了机器遗忘作为隐私保护手段的应用，但它们在面部检索（尤其是针对现代嵌入式识别模型）中的适用性仍然鲜有探讨。 在这项工作中，我们研究了面向检索系统的面部身份遗忘问题，并展示了其固有的挑战。我们的目标是通过分散选定身份的嵌入以使其无法被检索，在超球面上防止紧凑的身份聚类形成，从而阻止重新识别。主要挑战在于在保留嵌入空间的判别性结构和模型对剩余身份的检索性能的同时实现这种忘记效果。 为解决这一问题，我们评估了几种现有的近似类别遗忘方法（如随机标签法、梯度上升法、边界遗忘法及其他最近的方法）在面部检索中的应用，并提出了一种简单而有效的基于分散的遗忘策略。我们在标准基准测试集（VGGFace2和CelebA）上的广泛实验表明，我们的方法能够实现更优的忘记行为同时保持检索效用。
URL
https://arxiv.org/abs/2512.13317
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Detection of Digital Facial Retouching utilizing Face Beauty Information
2025-12-09 09:23:33Philipp Srock, Juan E. Tapia, Christoph BuschAbstract
Facial retouching to beautify images is widely spread in social media, advertisements, and it is even applied in professional photo studios to let individuals appear younger, remove wrinkles and skin impurities. Generally speaking, this is done to enhance beauty. This is not a problem itself, but when retouched images are used as biometric samples and enrolled in a biometric system, it is one. Since previous work has proven facial retouching to be a challenge for face recognition systems,the detection of facial retouching becomes increasingly necessary. This work proposes to study and analyze changes in beauty assessment algorithms of retouched images, assesses different feature extraction methods based on artificial intelligence in order to improve retouching detection, and evaluates whether face beauty can be exploited to enhance the detection rate. In a scenario where the attacking retouching algorithm is unknown, this work achieved 1.1% D-EER on single image detection.
Abstract (translated)
面部修图在社交媒体、广告中非常普遍，甚至专业摄影棚也使用这种方法让个人看起来更年轻、去除皱纹和皮肤瑕疵。通常来说，这种做法是为了提升美感，并无大碍；然而，当经过修饰的图像被用作生物识别样本并录入到生物识别系统时，问题就出现了。之前的研究已经证明面部修图对人脸识别系统构成了挑战，因此检测面部修图变得越来越必要。 本研究旨在探讨和分析修图后图像在美容评估算法中的变化，评估基于人工智能的不同特征提取方法以提高修图检测的准确性，并检验脸部美感是否可以被利用来提升检测率。在攻击性修图算法未知的情境下，该研究仅凭单张图片检测就能达到1.1%的虚警误报率（D-EER）。
URL
https://arxiv.org/abs/2512.08397
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Spoofing-aware Prompt Learning for Unified Physical-Digital Facial Attack Detection
2025-12-06 09:34:39Jiabao Guo, Yadian Wang, Hui Ma, Yuhao Fu, Ju Jia, Hui Liu, Shengeng Tang, Lechao Cheng, Yunfeng Diao, Ajian LiuAbstract
Real-world face recognition systems are vulnerable to both physical presentation attacks (PAs) and digital forgery attacks (DFs). We aim to achieve comprehensive protection of biometric data by implementing a unified physical-digital defense framework with advanced detection. Existing approaches primarily employ CLIP with regularization constraints to enhance model generalization across both tasks. However, these methods suffer from conflicting optimization directions between physical and digital attack detection under same category prompt spaces. To overcome this limitation, we propose a Spoofing-aware Prompt Learning for Unified Attack Detection (SPL-UAD) framework, which decouples optimization branches for physical and digital attacks in the prompt space. Specifically, we construct a learnable parallel prompt branch enhanced with adaptive Spoofing Context Prompt Generation, enabling independent control of optimization for each attack type. Furthermore, we design a Cues-awareness Augmentation that leverages the dual-prompt mechanism to generate challenging sample mining tasks on data, significantly enhancing the model's robustness against unseen attack types. Extensive experiments on the large-scale UniAttackDataPlus dataset demonstrate that the proposed method achieves significant performance improvements in unified attack detection tasks.
Abstract (translated)
现实世界中的面部识别系统容易受到物理展示攻击（PA）和数字伪造攻击（DF）的威胁。我们的目标是通过实施一个具有高级检测功能的统一物理-数字防御框架来实现生物特征数据的全面保护。现有的方法主要采用带有正则化约束条件的CLIP，以增强模型在两类任务中的泛化能力。然而，这些方法在同类别提示空间中存在针对物理和数字攻击检测优化方向冲突的问题。 为了解决这一局限性，我们提出了一个名为“基于伪造意识提示学习的一体化攻击检测框架”（SPL-UAD），该框架在提示空间中解耦了对物理和数字攻击的优化分支。具体而言，我们构建了一个可以通过自适应伪造上下文提示生成来增强的可学习并行提示分支，从而能够独立控制每种攻击类型的优化过程。 此外，我们设计了一种基于线索感知的数据增强机制，利用双提示机制在数据上生成挑战性的样本挖掘任务，显著提高了模型对未见过的攻击类型时的鲁棒性。在大规模UniAttackDataPlus数据集上的广泛实验表明，所提出的方法在统一攻击检测任务中取得了显著的性能改进。
URL
https://arxiv.org/abs/2512.06363
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A Comparative Study on Synthetic Facial Data Generation Techniques for Face Recognition
2025-12-05 18:11:29Pedro Vidal, Bernardo Biesseck, Luiz E. L. Coelho, Roger Granada, David MenottiAbstract
Facial recognition has become a widely used method for authentication and identification, with applications for secure access and locating missing persons. Its success is largely attributed to deep learning, which leverages large datasets and effective loss functions to learn discriminative features. Despite these advances, facial recognition still faces challenges in explainability, demographic bias, privacy, and robustness to aging, pose variations, lighting changes, occlusions, and facial expressions. Privacy regulations have also led to the degradation of several datasets, raising legal, ethical, and privacy concerns. Synthetic facial data generation has been proposed as a promising solution. It mitigates privacy issues, enables experimentation with controlled facial attributes, alleviates demographic bias, and provides supplementary data to improve models trained on real data. This study compares the effectiveness of synthetic facial datasets generated using different techniques in facial recognition tasks. We evaluate accuracy, rank-1, rank-5, and the true positive rate at a false positive rate of 0.01% on eight leading datasets, offering a comparative analysis not extensively explored in the literature. Results demonstrate the ability of synthetic data to capture realistic variations while emphasizing the need for further research to close the performance gap with real data. Techniques such as diffusion models, GANs, and 3D models show substantial progress; however, challenges remain.
Abstract (translated)
面部识别已成为认证和身份识别中广泛应用的方法，应用于安全访问和个人寻找等场景。其成功主要归功于深度学习技术，该技术利用大规模数据集和有效的损失函数来学习区分性特征。尽管取得了这些进展，面部识别仍然面临着解释性、人口统计学偏差、隐私以及应对年龄变化、姿势变换、光照改变、遮挡及表情差异等方面的挑战。随着隐私法规的出台，许多数据集也因此遭到破坏或无法使用，引发了法律、伦理和隐私方面的担忧。合成面部数据生成被提出作为解决这些问题的一个有前景的方法。它可以减轻隐私问题，使人们能够在控制的人脸属性条件下进行实验，缓解人口统计学偏差，并提供补充数据以改进基于真实数据训练的模型。本研究比较了采用不同技术生成的合成面部数据集在面部识别任务中的有效性。我们在八个领先的数据集中评估了准确率、第一匹配率（rank-1）、第五匹配率（rank-5）以及在0.01%错误接受率下的真阳性率，提供了文献中未被广泛探讨的一个对比分析。结果表明，合成数据能够捕捉到现实变化的特征，同时也强调了需要进一步研究以缩小与真实数据性能之间的差距。扩散模型、GANs和3D模型等技术已经取得了显著的进步；然而，依然存在挑战有待克服。
URL
https://arxiv.org/abs/2512.05928
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Mitigating Bias with Words: Inducing Demographic Ambiguity in Face Recognition Templates by Text Encoding
2025-12-05 10:41:58Tahar Chettaoui, Naser Damer, Fadi BoutrosAbstract
Face recognition (FR) systems are often prone to demographic biases, partially due to the entanglement of demographic-specific information with identity-relevant features in facial embeddings. This bias is extremely critical in large multicultural cities, especially where biometrics play a major role in smart city infrastructure. The entanglement can cause demographic attributes to overshadow identity cues in the embedding space, resulting in disparities in verification performance across different demographic groups. To address this issue, we propose a novel strategy, Unified Text-Image Embedding (UTIE), which aims to induce demographic ambiguity in face embeddings by enriching them with information related to other demographic groups. This encourages face embeddings to emphasize identity-relevant features and thus promotes fairer verification performance across groups. UTIE leverages the zero-shot capabilities and cross-modal semantic alignment of Vision-Language Models (VLMs). Given that VLMs are naturally trained to align visual and textual representations, we enrich the facial embeddings of each demographic group with text-derived demographic features extracted from other demographic groups. This encourages a more neutral representation in terms of demographic attributes. We evaluate UTIE using three VLMs, CLIP, OpenCLIP, and SigLIP, on two widely used benchmarks, RFW and BFW, designed to assess bias in FR. Experimental results show that UTIE consistently reduces bias metrics while maintaining, or even improving in several cases, the face verification accuracy.
Abstract (translated)
人脸识别（FR）系统常常存在人口统计学偏差，部分原因是面部嵌入中包含了特定于某一人群的信息与身份相关特征的纠缠。这种偏见在大型多元文化城市尤其严重，尤其是在生物识别技术对智慧城市基础设施发挥重要作用的情况下。这种纠缠可能导致人口统计属性在嵌入空间中压倒了身份线索，从而导致不同人口群体之间的验证性能差异。为解决这一问题，我们提出了一种新颖策略——统一文本图像嵌入（UTIE），旨在通过将与其它人口群体相关的特征信息加入面部嵌入来诱导出人口统计学的模糊性。这鼓励面部嵌入强调身份相关特征，从而促进不同群体间更公平的验证性能。 UTIE利用了视觉语言模型（VLM）的零样本能力和跨模态语义对齐能力。由于VLM自然地训练用于调整视觉和文本表示的一致性，我们用从其它人口统计学群组提取出的文字衍生的人口特征来丰富每个群体的面部嵌入。这鼓励了一个更中立的人口属性表示。 我们在CLIP、OpenCLIP 和 SigLIP 三种 VLM 上使用 RFW 和 BFW 这两个广泛使用的基准进行UTIE评估，后者设计用于测试 FR 中的偏见问题。实验结果显示 UTIE 在降低偏见指标的同时保持甚至在某些情况下提高了面部验证准确率。
URL
https://arxiv.org/abs/2512.08981
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Superpixel Attack: Enhancing Black-box Adversarial Attack with Image-driven Division Areas
2025-11-29 05:28:52Issa Oe, Keiichiro Yamamura, Hiroki Ishikura, Ryo Hamahira, Katsuki FujisawaAbstract
Deep learning models are used in safety-critical tasks such as automated driving and face recognition. However, small perturbations in the model input can significantly change the predictions. Adversarial attacks are used to identify small perturbations that can lead to misclassifications. More powerful black-box adversarial attacks are required to develop more effective defenses. A promising approach to black-box adversarial attacks is to repeat the process of extracting a specific image area and changing the perturbations added to it. Existing attacks adopt simple rectangles as the areas where perturbations are changed in a single iteration. We propose applying superpixels instead, which achieve a good balance between color variance and compactness. We also propose a new search method, versatile search, and a novel attack method, Superpixel Attack, which applies superpixels and performs versatile search. Superpixel Attack improves attack success rates by an average of 2.10% compared with existing attacks. Most models used in this study are robust against adversarial attacks, and this improvement is significant for black-box adversarial attacks. The code is avilable at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2512.02062
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GFT-GCN: Privacy-Preserving 3D Face Mesh Recognition with Spectral Diffusion
2025-11-25 06:07:26Hichem Felouat, Hanrui Wang, Isao EchizenAbstract
3D face recognition offers a robust biometric solution by capturing facial geometry, providing resilience to variations in illumination, pose changes, and presentation attacks. Its strong spoof resistance makes it suitable for high-security applications, but protecting stored biometric templates remains critical. We present GFT-GCN, a privacy-preserving 3D face recognition framework that combines spectral graph learning with diffusion-based template protection. Our approach integrates the Graph Fourier Transform (GFT) and Graph Convolutional Networks (GCN) to extract compact, discriminative spectral features from 3D face meshes. To secure these features, we introduce a spectral diffusion mechanism that produces irreversible, renewable, and unlinkable templates. A lightweight client-server architecture ensures that raw biometric data never leaves the client device. Experiments on the BU-3DFE and FaceScape datasets demonstrate high recognition accuracy and strong resistance to reconstruction attacks. Results show that GFT-GCN effectively balances privacy and performance, offering a practical solution for secure 3D face authentication.
Abstract (translated)
URL
https://arxiv.org/abs/2511.19958
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Face, Whole-Person, and Object Classification in a Unified Space Via The Interleaved Multi-Domain Identity Curriculum
2025-11-25 02:23:10Thomas M Metz, Matthew Q Hill, Alice J O'TooleAbstract
Vision foundation models can perform generalized object classification in zero-shot mode, and face/person recognition when they are fine-tuned. However, fine-tuned models suffer from catastrophic forgetting. We create models that perform four tasks (object recognition, face recognition from high- and low-quality images, and person recognition from whole-body images) in a single embedding space -- without incurring substantial catastrophic forgetting. To accomplish this, we introduce two variants of the Interleaved Multi-Domain Identity Curriculum (IMIC): a gradient-coupled, interleaving training schedule that fine-tunes a foundation backbone simultaneously on all four tasks. The IMIC method proved effective with three foundation model bases: DINOv3, CLIP, and EVA-02. Two of these (EVA-02 and CLIP) performed comparably with domain experts on all four tasks concurrently and were more accurate than humans at multi-tasking across face, body, and object datasets. Further, we demonstrate that our approach does not substantially harm out-of-distribution generalization, thus maintaining a key property of foundation models. Analysis of the most accurate model variants (EVA-02 + IMIC A and B) showed linearly separable representations of the four tasks in the unified embedding space, but with substantial sharing of features across tasks. Fewer than 100 PCs calculated from any one task could perform all other tasks with nearly zero performance degradation.
Abstract (translated)
URL
https://arxiv.org/abs/2511.19846
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Quality-Controlled Multimodal Emotion Recognition in Conversations with Identity-Based Transfer Learning and MAMBA Fusion
2025-11-18 23:24:27Zanxu Wang, Homayoon BeigiAbstract
This paper addresses data quality issues in multimodal emotion recognition in conversation (MERC) through systematic quality control and multi-stage transfer learning. We implement a quality control pipeline for MELD and IEMOCAP datasets that validates speaker identity, audio-text alignment, and face detection. We leverage transfer learning from speaker and face recognition, assuming that identity-discriminative embeddings capture not only stable acoustic and Facial traits but also person-specific patterns of emotional expression. We employ RecoMadeEasy(R) engines for extracting 512-dimensional speaker and face embeddings, fine-tune MPNet-v2 for emotion-aware text representations, and adapt these features through emotion-specific MLPs trained on unimodal datasets. MAMBA-based trimodal fusion achieves 64.8% accuracy on MELD and 74.3% on IEMOCAP. These results show that combining identity-based audio and visual embeddings with emotion-tuned text representations on a quality-controlled subset of data yields consistent competitive performance for multimodal emotion recognition in conversation and provides a basis for further improvement on challenging, low-frequency emotion classes.
Abstract (translated)
URL
https://arxiv.org/abs/2511.14969
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LoRA-Enhanced Vision Transformer for Single Image based Morphing Attack Detection via Knowledge Distillation from EfficientNet
2025-11-16 13:58:11Ria Shekhawat, Sushrut Patwardhan, Raghavendra Ramachandra, Praveen Kumar Chandaliya, Kishor P. UplaAbstract
Face Recognition Systems (FRS) are critical for security but remain vulnerable to morphing attacks, where synthetic images blend biometric features from multiple individuals. We propose a novel Single-Image Morphing Attack Detection (S-MAD) approach using a teacher-student framework, where a CNN-based teacher model refines a ViT-based student model. To improve efficiency, we integrate Low-Rank Adaptation (LoRA) for fine-tuning, reducing computational costs while maintaining high detection accuracy. Extensive experiments are conducted on a morphing dataset built from three publicly available face datasets, incorporating ten different morphing generation algorithms to assess robustness. The proposed method is benchmarked against six state-of-the-art S-MAD techniques, demonstrating superior detection performance and computational efficiency.
Abstract (translated)
URL
https://arxiv.org/abs/2511.12602
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Batch Transformer Architecture: Case of Synthetic Image Generation for Emotion Expression Facial Recognition
2025-11-13 19:45:41Stanislav SelitskiyAbstract
A novel Transformer variation architecture is proposed in the implicit sparse style. Unlike "traditional" Transformers, instead of attention to sequential or batch entities in their entirety of whole dimensionality, in the proposed Batch Transformers, attention to the "important" dimensions (primary components) is implemented. In such a way, the "important" dimensions or feature selection allows for a significant reduction of the bottleneck size in the encoder-decoder ANN architectures. The proposed architecture is tested on the synthetic image generation for the face recognition task in the case of the makeup and occlusion data set, allowing for increased variability of the limited original data set.
Abstract (translated)
URL
https://arxiv.org/abs/2511.11754
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Introducing Nylon Face Mask Attacks: A Dataset for Evaluating Generalised Face Presentation Attack Detection
2025-11-11 11:13:12Manasa, Sushrut Patwardhan, Narayan Vetrekar, Pavan Kumar, R. S. Gad, Raghavendra RamachandraAbstract
Face recognition systems are increasingly deployed across a wide range of applications, including smartphone authentication, access control, and border security. However, these systems remain vulnerable to presentation attacks (PAs), which can significantly compromise their reliability. In this work, we introduce a new dataset focused on a novel and realistic presentation attack instrument called Nylon Face Masks (NFMs), designed to simulate advanced 3D spoofing scenarios. NFMs are particularly concerning due to their elastic structure and photorealistic appearance, which enable them to closely mimic the victim's facial geometry when worn by an attacker. To reflect real-world smartphone-based usage conditions, we collected the dataset using an iPhone 11 Pro, capturing 3,760 bona fide samples from 100 subjects and 51,281 NFM attack samples across four distinct presentation scenarios involving both humans and mannequins. We benchmark the dataset using five state-of-the-art PAD methods to evaluate their robustness under unseen attack conditions. The results demonstrate significant performance variability across methods, highlighting the challenges posed by NFMs and underscoring the importance of developing PAD techniques that generalise effectively to emerging spoofing threats.
Abstract (translated)
URL
https://arxiv.org/abs/2511.08114
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StableMorph: High-Quality Face Morph Generation with Stable Diffusion
2025-11-11 10:44:41Wassim Kabbani, Kiran Raja, Raghavendra Ramachandra, Christoph BuschAbstract
Face morphing attacks threaten the integrity of biometric identity systems by enabling multiple individuals to share a single identity. To develop and evaluate effective morphing attack detection (MAD) systems, we need access to high-quality, realistic morphed images that reflect the challenges posed in real-world scenarios. However, existing morph generation methods often produce images that are blurry, riddled with artifacts, or poorly constructed making them easy to detect and not representative of the most dangerous attacks. In this work, we introduce StableMorph, a novel approach that generates highly realistic, artifact-free morphed face images using modern diffusion-based image synthesis. Unlike prior methods, StableMorph produces full-head images with sharp details, avoids common visual flaws, and offers unmatched control over visual attributes. Through extensive evaluation, we show that StableMorph images not only rival or exceed the quality of genuine face images but also maintain a strong ability to fool face recognition systems posing a greater challenge to existing MAD solutions and setting a new standard for morph quality in research and operational testing. StableMorph improves the evaluation of biometric security by creating more realistic and effective attacks and supports the development of more robust detection systems.
Abstract (translated)
URL
https://arxiv.org/abs/2511.08090
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Hybrid second-order gradient histogram based global low-rank sparse regression for robust face recognition
2025-11-08 07:31:38Hongxia Li, Ying Ji, Yongxin Dong, Yuehua FengAbstract
Low-rank sparse regression models have been widely applied in the field of face recognition. To further address the challenges caused by complex occlusions and illumination variations, this paper proposes a Hybrid Second-Order Gradient Histogram based Global Low-Rank Sparse Regression (H2H-GLRSR) model. Specifically, a novel feature descriptor called the Hybrid Second-Order Gradient Histogram (H2H) is first designed to more effectively characterize the local structural features of facial images. Then, this descriptor is integrated with the Sparse Regularized Nuclear Norm based Matrix Regression (SR$\_$NMR). Moreover, a global low-rank constraint is imposed on the residual matrix, enabling the model to better capture the global correlations inherent in structured noise. Experimental results demonstrate that the proposed method significantly outperforms existing regression-based classification approaches under challenging scenarios involving occlusions, illumination changes, and unconstrained environments.
Abstract (translated)
URL
https://arxiv.org/abs/2511.05893
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Photo Dating by Facial Age Aggregation
2025-11-07 18:08:04Jakub Paplham, Vojtech FrancAbstract
We introduce a novel method for Photo Dating which estimates the year a photograph was taken by leveraging information from the faces of people present in the image. To facilitate this research, we publicly release CSFD-1.6M, a new dataset containing over 1.6 million annotated faces, primarily from movie stills, with identity and birth year annotations. Uniquely, our dataset provides annotations for multiple individuals within a single image, enabling the study of multi-face information aggregation. We propose a probabilistic framework that formally combines visual evidence from modern face recognition and age estimation models, and career-based temporal priors to infer the photo capture year. Our experiments demonstrate that aggregating evidence from multiple faces consistently improves the performance and the approach significantly outperforms strong, scene-based baselines, particularly for images containing several identifiable individuals.
Abstract (translated)
URL
https://arxiv.org/abs/2511.05464
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Elements of Active Continuous Learning and Uncertainty Self-Awareness: a Narrow Implementation for Face and Facial Expression Recognition
2025-11-04 17:01:53Stanislav SelitskiyAbstract
Reflection on one's thought process and making corrections to it if there exists dissatisfaction in its performance is, perhaps, one of the essential traits of intelligence. However, such high-level abstract concepts mandatory for Artificial General Intelligence can be modelled even at the low level of narrow Machine Learning algorithms. Here, we present the self-awareness mechanism emulation in the form of a supervising artificial neural network (ANN) observing patterns in activations of another underlying ANN in a search for indications of the high uncertainty of the underlying ANN and, therefore, the trustworthiness of its predictions. The underlying ANN is a convolutional neural network (CNN) ensemble employed for face recognition and facial expression tasks. The self-awareness ANN has a memory region where its past performance information is stored, and its learnable parameters are adjusted during the training to optimize the performance. The trustworthiness verdict triggers the active learning mode, giving elements of agency to the machine learning algorithm that asks for human help in high uncertainty and confusion conditions.
Abstract (translated)
URL
https://arxiv.org/abs/2511.05574
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Robust Face Liveness Detection for Biometric Authentication using Single Image
2025-11-04 15:13:03Poulami Raha, Yeongnam ChaeAbstract
Biometric technologies are widely adopted in security, legal, and financial systems. Face recognition can authenticate a person based on the unique facial features such as shape and texture. However, recent works have demonstrated the vulnerability of Face Recognition Systems (FRS) towards presentation attacks. Using spoofing (aka.,presentation attacks), a malicious actor can get illegitimate access to secure systems. This paper proposes a novel light-weight CNN framework to identify print/display, video and wrap attacks. The proposed robust architecture provides seamless liveness detection ensuring faster biometric authentication (1-2 seconds on CPU). Further, this also presents a newly created 2D spoof attack dataset consisting of more than 500 videos collected from 60 subjects. To validate the effectiveness of this architecture, we provide a demonstration video depicting print/display, video and wrap attack detection approaches. The demo can be viewed in the following link: this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2511.02645
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PSTF-AttControl: Per-Subject-Tuning-Free Personalized Image Generation with Controllable Face Attributes
2025-10-29 01:42:23Xiang liu, Zhaoxiang Liu, Huan Hu, Zipeng Wang, Ping Chen, Zezhou Chen, Kai Wang, Shiguo LianAbstract
Recent advancements in personalized image generation have significantly improved facial identity preservation, particularly in fields such as entertainment and social media. However, existing methods still struggle to achieve precise control over facial attributes in a per-subject-tuning-free (PSTF) way. Tuning-based techniques like PreciseControl have shown promise by providing fine-grained control over facial features, but they often require extensive technical expertise and additional training data, limiting their accessibility. In contrast, PSTF approaches simplify the process by enabling image generation from a single facial input, but they lack precise control over facial attributes. In this paper, we introduce a novel, PSTF method that enables both precise control over facial attributes and high-fidelity preservation of facial identity. Our approach utilizes a face recognition model to extract facial identity features, which are then mapped into the $W^+$ latent space of StyleGAN2 using the e4e encoder. We further enhance the model with a Triplet-Decoupled Cross-Attention module, which integrates facial identity, attribute features, and text embeddings into the UNet architecture, ensuring clean separation of identity and attribute information. Trained on the FFHQ dataset, our method allows for the generation of personalized images with fine-grained control over facial attributes, while without requiring additional fine-tuning or training data for individual identities. We demonstrate that our approach successfully balances personalization with precise facial attribute control, offering a more efficient and user-friendly solution for high-quality, adaptable facial image synthesis. The code is publicly available at this https URL.
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URL
https://arxiv.org/abs/2510.25084
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Switchable Token-Specific Codebook Quantization For Face Image Compression
2025-10-27 02:56:17Yongbo Wang, Haonan Wang, Guodong Mu, Ruixin Zhang, Jiaqi Chen, Jingyun Zhang, Jun Wang, Yuan Xie, Zhizhong Zhang, Shouhong DingAbstract
With the ever-increasing volume of visual data, the efficient and lossless transmission, along with its subsequent interpretation and understanding, has become a critical bottleneck in modern information systems. The emerged codebook-based solution utilize a globally shared codebook to quantize and dequantize each token, controlling the bpp by adjusting the number of tokens or the codebook size. However, for facial images, which are rich in attributes, such global codebook strategies overlook both the category-specific correlations within images and the semantic differences among tokens, resulting in suboptimal performance, especially at low bpp. Motivated by these observations, we propose a Switchable Token-Specific Codebook Quantization for face image compression, which learns distinct codebook groups for different image categories and assigns an independent codebook to each token. By recording the codebook group to which each token belongs with a small number of bits, our method can reduce the loss incurred when decreasing the size of each codebook group. This enables a larger total number of codebooks under a lower overall bpp, thereby enhancing the expressive capability and improving reconstruction performance. Owing to its generalizable design, our method can be integrated into any existing codebook-based representation learning approach and has demonstrated its effectiveness on face recognition datasets, achieving an average accuracy of 93.51% for reconstructed images at 0.05 bpp.
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URL
https://arxiv.org/abs/2510.22943
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Reliable and Reproducible Demographic Inference for Fairness in Face Analysis
2025-10-23 12:22:02Alexandre Fournier-Montgieux, Herv\'e Le Borgne, Adrian Popescu, Bertrand LuvisonAbstract
Fairness evaluation in face analysis systems (FAS) typically depends on automatic demographic attribute inference (DAI), which itself relies on predefined demographic segmentation. However, the validity of fairness auditing hinges on the reliability of the DAI process. We begin by providing a theoretical motivation for this dependency, showing that improved DAI reliability leads to less biased and lower-variance estimates of FAS fairness. To address this, we propose a fully reproducible DAI pipeline that replaces conventional end-to-end training with a modular transfer learning approach. Our design integrates pretrained face recognition encoders with non-linear classification heads. We audit this pipeline across three dimensions: accuracy, fairness, and a newly introduced notion of robustness, defined via intra-identity consistency. The proposed robustness metric is applicable to any demographic segmentation scheme. We benchmark the pipeline on gender and ethnicity inference across multiple datasets and training setups. Our results show that the proposed method outperforms strong baselines, particularly on ethnicity, which is the more challenging attribute. To promote transparency and reproducibility, we will publicly release the training dataset metadata, full codebase, pretrained models, and evaluation toolkit. This work contributes a reliable foundation for demographic inference in fairness auditing.
Abstract (translated)
面部分析系统（FAS）中的公平性评估通常依赖于自动人口属性推断（DAI），而后者又基于预定义的人口细分。然而，公平审计的有效性取决于DAI过程的可靠性。我们首先提供这种依赖关系的理论动机，并表明改进了DAI的可靠性可以减少对FAS公平性的偏差和方差估计。为了解决这一问题，我们提出了一种完全可重现的DAI流水线，该流水线用模块化的迁移学习方法代替传统的端到端训练。我们的设计将预训练的人脸识别编码器与非线性分类头部相结合。我们在准确率、公平性和一种新引入的鲁棒性的概念上对这一流程进行审核，后者通过同一身份内的一致性来定义。我们提出的鲁棒性度量适用于任何人口细分方案。 在多个数据集和训练设置下，我们对该流水线进行了性别和族裔推断基准测试。实验结果显示，所提出的方法优于强大的基线方法，在更难处理的族裔属性上尤其如此。为了促进透明性和可重现性，我们将公开发布用于训练的数据集元信息、完整代码库、预训练模型以及评估工具包。 这项工作为公平审计中的人口统计推断提供了一个可靠的基石。
URL
https://arxiv.org/abs/2510.20482
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