Facial_Landmark
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FreqFLD: Towards All-in-One Facial Landmark Detection via Frequency Modulation
2026-09-09 14:58:46Shun Ren, Kaijie Jin, Shengkai Hu, Beihang Song, Hang Sun, Wenwen Min, Youfa Liu, Jun WanAbstract
Recent progress in deep learning has significantly advanced facial landmark detection. However, most existing methods process features in a spatial-domain manner under a dataset-specific training paradigm, which overlooks the fact that facial landmark detection is inherently geometry-driven and sensitive to frequency variations, thereby limiting cross-dataset generalization under complex scenarios and hindering the development of a facial landmark detection model. To address this issue, we propose \textbf{FreqFLD}, a \textbf{freq}uency-modulated framework towards All-in-One \textbf{f}acial \textbf{l}andmark \textbf{d}etection. Specifically, FreqFLD introduces a Frequency Modulation Module (FreqMoM) to explicitly induce the frequency prior by decoupling and modulating low- and high-frequency components, which is then injected into subsequent feature modeling to enable balanced modeling of global facial structure and local landmark details. Furthermore, FreqFLD employs a Frequency-Modulated Mixture-of-Experts (FreqMoE), with expert selection adaptively conditioned on frequency-modulated priors, enabling flexible modeling of heterogeneous facial landmark patterns under diverse and challenging scenarios. To regularize frequency-consistent modeling under the All-in-One paradigm, we further introduce a Frequency-Consistent Routing (FreqCR) loss, which constrains the routing and assignment of frequency-aware experts to promote balanced expert utilization across diverse facial scenarios, thereby enabling stable expert specialization and achieving robust facial landmark detection. Extensive experiments demonstrate that the proposed FreqFLD achieves comparable performance on popular datasets. The code is available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2609.10278
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3D Weighted Geometric Graph Neural Networks for Sheep Facial Pain Assessment
2026-08-11 15:21:36Alam Noor, Luis Almeida, Mohamed DaoudiAbstract
Deep learning systems perform mainly within the 2D for a single image domain and take the face as a single-dimension representation, losing sight of the 3D anatomy of sheep and cross-landmark spatial relationships that are intrinsic to the clinically proven Sheep Pain Facial Expression Scale (SPFES). This paper presents the \textbf{3D Sheep Pain Facial Expression System (3D-SPFES)}, a novel, monocular depth-aware geometric graph neural network system that integrates each SPFES facial landmark, such as the ears, eyes, and nose, into 3D Euclidean space estimated from a single RGB camera by using VideoDepthAnything, thus preventing the need for specialized depth hardware. Each landmark node includes a feature vector containing its 3D spatial coordinates, estimated surface normal, and facial attribute class embedding. Edges linked to nodes are assigned weights based on an aggregate metric that combines both Euclidean distance and surface co-planarity in a 3D space. A Weighted Geometric Graph Neural Network (WG-GNN) studies this graph using $\mathcal{K} = 3$ geometry-aware message-passing layers enhanced by a scaled dot-product attention method that selectively enhances anatomically relevant inter-landmark messages. The resultant node embeddings are combined into $\mathcal{O} = 3$ pain-level clusters and integrated into a Normalized Pain Score (NPS) within the range of $[0, 100%]$ a confidence-weighted, SPFES-derived scoring method.
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URL
https://arxiv.org/abs/2608.11050
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SCALP: Semi-Supervised Statistical Shape Modeling from Imperfect 3D Photogrammetry via Landmark-Anchored Spectral Warp
2026-07-31 18:09:46Nawazish Khan, Sanjay Bhandari, Sarang Joshi, Alzbeta Novotna, Tiffany Jeong, Loretta Bowman, Michael Hernandez, Tobi Somorin, Viraj Govani, Jesse Glodstein, Shireen ElhabianAbstract
Correspondence-based statistical shape modeling (SSM) is vital for population-level morphometric analysis, but conventional pipelines assume clean, fully registered surfaces. Real-world clinical photogrammetry scans are often noisy, partial, and cluttered, hindering the adoption of radiation-free surface imaging as a safe alternative to computed tomography (CT) for infant craniosynostosis. We present SCALP (Semi-supervised Correspondence via lAndmark Localization and sPectral warping), a two-stage framework that constructs consistent shape models directly from raw, imperfect surface scans. First, a semi-supervised Point Transformer leverages a small expert-annotated dataset alongside a large unlabeled cohort to accurately localize craniofacial landmarks with minimal annotation overhead. Second, these landmarks anchor a Laplace--Beltrami spectral deformation of an anatomical template, generating dense correspondences while naturally isolating the cranium from peripheral scanning clutter without manual preprocessing. Experiments on infant photogrammetry scans demonstrate that SCALP consistently outperforms state-of-the-art unsupervised point-cloud approaches, offering a clinically practical pathway toward objective, radiation-free head shape analysis.
Abstract (translated)
URL
https://arxiv.org/abs/2608.00187
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Collaborative Feature Aggregation for Face Super-Resolution and Robust Re-Identification
2026-07-30 12:39:57Juheon Hwang, Taewan Kim, Jiwoo KangAbstract
We propose a novel collaborative approach for face super-resolution (SR) and robust person re-identification from sequential or multi-view facial images. Traditional SR methods often suffer from blurring and distortion in faces recovered from poor-quality images due to low resolution. Image- and video-based facial SR methods using facial landmarks or segmentation also have similar challenges. To overcome these limitations, we leverage multiple correlated facial observations, across time or viewpoints, by introducing a transformer-based collaborative feature aggregation method that unifies identity features from multi-sequence or multi-view data. This allows faces in multiple sequences of an individual to contribute to accurately estimating common facial features. Furthermore, we propose a cascade SR network to progressively restore the high-resolution image of the target's face with gradual facial feature unification. The unified identity representation is further utilized in person re-identification scenarios, enabling accurate matching even under severe image degradation. The exhaustive experimental results and comparisons show that our method outperforms other state-of-the-art methods, demonstrating consistent improvements in both face super-resolution and re-identification performance. Our work highlights the effectiveness of joint identity reconstruction and progressive image restoration from multiple facial inputs in enhancing downstream visual recognition tasks.
Abstract (translated)
URL
https://arxiv.org/abs/2607.28130
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Real-Time EEG Cap Electrode Detection for Guided Point-of-Care Placement
2026-07-22 13:44:22William Lehn-Schi{\o}ler, Mads Sverker Nilsson, Nicki Skafte DetlefsenAbstract
We present a two-stage vision system that detects EEG cap electrodes in a live webcam stream and validates their anatomical placement in real time. A single-class YOLO detector localises electrodes; a geometric stage assigns each detection to a named 10-20 role from facial landmarks. Evaluating under subject-disjoint leave-one-subject-out (LOSO) cross-validation across five subjects wearing the clinically-validated Small/Medium/Large caps, the detector attains mAP@.5 = 0.94 +/- 0.07 across five held-out folds (0.96 pooled). A dedicated leave-one-cap-out axis, holding out every frame of a cap regardless of subject, leaves Medium and Large mAP@.5 within 0.01 of LOSO (0.97, 0.97) while Small drops to 0.72 +/- 0.28, a gap confounded with subject familiarity rather than cap style. Geometric augmentation (rotation, perspective, mixup) improves in-plane-roll robustness and temporal-electrode recall at no inference cost, and a landmark-driven head crop extends the usable distance range, lifting mAP@.5 from 0.23 to 0.45 at 0.6 x apparent scale. A compact mobile-candidate backbone (YOLOv10n) keeps the detector at real-time throughput (19 FPS) on a commodity CPU at 640 px.
Abstract (translated)
URL
https://arxiv.org/abs/2607.20142
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Video-Based Detection of squint and cataract for accessibility-aware adaptive web interface rendering
2026-07-08 07:33:40Amar Ranjan Dash, Manas Ranjan PatraAbstract
Squint and cataract are major ocular disorders that majorly affect visual perception and interaction capability. This paper proposes a real-time video-based automated detection system for squint and cataract detection based on computer vision and image processing methods. The proposed system uses a media-pipe face-mesh (a 478-point facial landmark detection model) to extract geometric ocular features for multi-class squint classification. Simultaneously, The presence and severity cataract is estimated through grayscale intensity and histogram-based lens opacity analysis. The system records short video sequences with standard laptop or mobile cameras, which can be deployed at low costs and on a large scale. The experimental performance has shown great accuracy in the detection of squint (98.39%) and classification of cataract (96.90%). Besides automatic ocular analysis, the proposed framework is also made accessible for visual impairment inference which will be integrated with future adaptive user interface and Web accessibility systems for people with visual impairment.
Abstract (translated)
URL
https://arxiv.org/abs/2607.07099
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SGF-CDNet: A Consistency-Discrepancy Graph Network over Semantic-Geometric Fused Nodes for Face Forgery Detection
2026-07-04 13:59:59Jiayao Jiang, Bin Liu, Nenghai YuAbstract
The rapid advancement of deepfakes necessitates robust face forgery detection. Although forged faces may lack obvious artifacts, they often contain subtle disharmony among different facial regions. We propose SGF-CDNet, a Consistency-Discrepancy Graph Network (CD-GNN) over Semantic-Geometric Fused (SGF) nodes. First, SGF-CDNet constructs SGF nodes by deeply fusing semantic regions from face parsing with geometric information from facial landmarks, allowing nodes to capture both high-level concepts and precise geometric constraints. Next, a dual-path CD-GNN performs parallel relational reasoning on these nodes across two dimensions: consistency and discrepancy. The consistency path evaluates if facial components follow natural biological patterns, while the discrepancy path mines for structural tensions and feature conflicts introduced by forgeries. By integrating these processes, our model effectively identifies disharmonious relationships between facial components. Extensive experiments on public datasets demonstrate that SGF-CDNet achieves superior performance, establishing it as a reliable solution for face forgery detection.
Abstract (translated)
URL
https://arxiv.org/abs/2607.03883
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LaCoVL-FER: Landmark-Guided Contrastive Learning Network with Vision-Language Enhancement for Facial Expression Recognition
2026-05-19 13:15:41Jiaxin Wang, Muwei Jian, Hui Yu, Junyu Dong, Yifan XiaAbstract
Facial Expression Recognition (FER) in the wild is still challenging due to uncontrolled variations in pose, occlusion, and illumination. Most existing attention-based methods primarily rely on visual appearance cues, suffering from attention redundancy and instability, which limits their performance in complex scenarios. To address these issues, we propose a novel landmark-guided contrastive learning network with vision-language enhancement for FER (LaCoVL-FER), which integrates geometric priors from facial landmarks and semantic priors from a vision-language model. Specifically, a Landmark-Guided Adaptive Encoder (LGAE) is designed to introduce geometric priors through a Bi-branch Gated Cross Attention (BGCA) mechanism, which achieves adaptive fusion of landmark-based geometric and visual appearance features to produce expression-relevant features, thereby focusing on key facial regions and suppressing noise interference. In parallel, a Vision-Language Enhancement Strategy (VLES) is presented to leverage the expression-relevant features to refine the generalizable visual features extracted by the frozen pretrained CLIP image encoder, yielding expression-specific visual representations. Based on these representations, an Expression-Conditioned Prompting (ECP) mechanism is utilized to further adapt the textual features of fixed class-level prompts from the frozen pretrained CLIP text encoder, generating more instance-aware textual representations. These visual-textual representations are aligned as semantic priors to enhance the robustness and generalization of FER. Quantitative and qualitative experiments demonstrate that our LaCoVL-FER outperforms state-of-the-art methods on three representative real-world FER datasets, including RAF-DB, FERPlus, and AffectNet. The code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2605.19821
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BIDO: A Biometric Identity Online Authentication Framework
2026-05-16 09:49:10Aditya Mithra, Sibi Chakkaravarthy S, Srinivas KankanalaAbstract
Security systems demand continuous, cryptograph- ically robust identity verification without requiring subjects to carry physical tokens, smart cards, or dedicated hardware authenticators. This paper presents BIDO (Biometric Identity Online), a device-free authentication standard that achieves Au- thenticator Assurance Level 2 (AAL2) per NIST SP 800-63B with- out storing long-lived biometric templates, facial images, or any other form of Personally Identifiable Information (PII). BIDO derives Elliptic Curve Digital Signature Algorithm (ECDSA) key material deterministically from a live biometric measurement salted with a user-defined memorized secret at every authen- tication event, eliminating persistent private-key storage while enabling verification from any commodity sensor terminal. The generated credentials are non-discoverable (non-resident) Web Authentication (WebAuthn) credentials, fully compatible with all FIDO2-enabled websites and services without modification on the server side. A multi-stage pipeline, comprising capture of 200 valid biometric samples, feature extraction using the Dlib 68- point facial landmark predictor, affine face alignment, frontality gating, Euclidean distance computation from the inter-eye mid- point, floor-division quantization with divisor q = 8, inter-session drift stabilization, and majority-voting SHA-256 hash binding, produces a Verification Seed (Vseed) from which the WebAuthn credential is transiently derived and immediately zeroized after signing. Evaluated against three prominent face benchmarks (VGGFace2, LFW, and MegaFace), achieving 99.51% verification accuracy on LFW and 92.14% Rank-1 identification accuracy on MegaFace Challenge 1 at 10^6 distractors, with a cryptographic False Accept Rate (FAR) of 0.03%, a False Reject Rate (FRR) of 0.90%.
Abstract (translated)
URL
https://arxiv.org/abs/2605.16908
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WorldComp2D: Spatio-semantic Representations of Object Identity and Location from Local Views
2026-05-12 08:21:41SeongMin Jin, Doo Seok JeongAbstract
Learning latent representations that capture both semantic and spatial information is central to efficient spatio-semantic reasoning. However, many existing approaches rely on implicit latent structures combined with dense feature maps or task-specific heads, limiting computational efficiency and flexibility. We propose WorldComp2D, a novel lightweight representation learning framework that explicitly structures latent space geometry according to object identity and spatial proximity using multiscale local receptive fields. This framework consists of (i) a proximity-dependent encoder that maps a given observation into a spatio-semantic latent space and (ii) a localizer that infers the coordinates of objects in the input from the resulting spatio-semantic representation. Using facial landmark localization as a proof-of-concept, we show that, compared to SoTA lightweight models, WorldComp2D reduces the numbers of parameters and FLOPs by up to 4.0X and 2.2X, respectively, while maintaining real-time performance on CPU. These results demonstrate that explicitly structured latent spaces provide an efficient and general foundation for spatio-semantic reasoning. This framework is open-sourced at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2605.11743
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GLEaN: A Text-to-image Bias Detection Approach for Public Comprehension
2026-04-10 21:42:29Bochu Ding, Brinnae Bent, Augustus WendellAbstract
Text-to-image (T2I) models, and their encoded biases, increasingly shape the visual media the public encounters. While researchers have produced a rich body of work on bias measurement, auditing, and mitigation in T2I systems, those methods largely target technical stakeholders, leaving a gap in public legibility. We introduce GLEaN (Generative Likeness Evaluation at N-Scale), a portrait-based explainability pipeline designed to make T2I model biases visually understandable to a broad audience. GLEaN comprises three stages: automated large-scale image generation from identity prompts, facial landmark-based filtering and spatial alignment, and median-pixel composition that distills a model's central tendency into a single representative portrait. The resulting composites require no statistical background to interpret; a viewer can see, at a glance, who a model 'imagines' when prompted with 'a doctor' versus a 'felon.' We demonstrate GLEaN on Stable Diffusion XL across 40 social and occupational identity prompts, producing composites that reproduce documented biases and surface new associations between skin tone and predicted emotion. We find in a between-subjects user study (N = 291) that GLEaN portraits communicate biases as effectively as conventional data tables, but require significantly less viewing time. Because the method relies solely on generated outputs, it can also be replicated on any black-box and closed-weight systems without access to model internals. GLEaN offers a scalable, model-agnostic approach to bias explainability, purpose-built for public comprehension, and is publicly available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2604.09923
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Auditing Demographic Bias in Facial Landmark Detection for Fair Human-Robot Interaction
2026-04-08 11:22:18Pablo Parte, Roberto Valle, Jos\'e M. Buenaposada, Luis BaumelaAbstract
Fairness in human-robot interaction critically depends on the reliability of the perceptual models that enable robots to interpret human behavior. While demographic biases have been widely studied in high-level facial analysis tasks, their presence in facial landmark detection remains unexplored. In this paper, we conduct a systematic audit of demographic bias in this task, analyzing the age, gender and race biases. To this end we introduce a controlled statistical methodology to disentangle demographic effects from confounding visual factors. Evaluations of a standard representative model demonstrate that confounding visual factors, particularly head pose and image resolution, heavily outweigh the impact of demographic attributes. Notably, after accounting for these confounders, we show that performance disparities across gender and race vanish. However, we identify a statistically significant age-related effect, with higher biases observed for older individuals. This shows that fairness issues can emerge even in low-level vision components and can propagate through the HRI pipeline, disproportionately affecting vulnerable populations. We argue that auditing and correcting such biases is a necessary step toward trustworthy and equitable robot perception systems.
Abstract (translated)
URL
https://arxiv.org/abs/2604.06961
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RDFace: A Benchmark Dataset for Rare Disease Facial Image Analysis under Extreme Data Scarcity and Phenotype-Aware Synthetic Generation
2026-04-03 20:56:30Ganlin Feng, Yuxi Long, Hafsa Ali, Erin Lou, Fahad Butt, Qian Liu, Yang Wang, Pingzhao HuAbstract
Rare diseases often manifest with distinctive facial phenotypes in children, offering valuable diagnostic cues for clinicians and AI-assisted screening systems. However, progress in this field is severely limited by the scarcity of curated, ethically sourced facial data and the high similarity among phenotypes across different conditions. To address these challenges, we introduce RDFace, a curated benchmark dataset comprising 456 pediatric facial images spanning 103 rare genetic conditions (average 4.4 samples per condition). Each ethically verified image is paired with standardized metadata. RDFace enables the development and evaluation of data-efficient AI models for rare disease diagnosis under real-world low-data constraints. We benchmark multiple pretrained vision backbones using cross-validation and explore synthetic augmentation with DreamBooth and FastGAN. Generated images are filtered via facial landmark similarity to maintain phenotype fidelity and merged with real data, improving diagnostic accuracy by up to 13.7% in ultra-low-data regimes. To assess semantic validity, phenotype descriptions generated by a vision-language model from real and synthetic images achieve a report similarity score of 0.84. RDFace establishes a transparent, benchmark-ready dataset for equitable rare disease AI research and presents a scalable framework for evaluating both diagnostic performance and the integrity of synthetic medical imagery.
Abstract (translated)
罕见病在儿童中常表现为独特的 facial phenotypes（面部表型），为临床医生和人工智能辅助筛查系统提供了宝贵的诊断线索。然而，该领域的发展严重受限于：符合伦理规范的面部数据稀缺，且不同疾病间的表型高度相似。为应对这些挑战，我们推出了 RDFace——一个经过精心整理的基准数据集，包含 456 张儿科面部图像，涵盖 103 种罕见遗传病（平均每种疾病 4.4 个样本）。每张符合伦理验证的图像均配有标准化元数据。RDFace 使得在真实世界低数据约束下，开发和评估数据高效的人工智能模型用于罕见病诊断成为可能。我们通过交叉验证对多个预训练视觉骨干网络进行了基准测试，并探索了使用 DreamBooth 和 FastGAN 进行合成数据增强。生成的图像通过面部关键点相似度进行筛选以保持表型真实性，并与真实数据合并，在极低数据场景下将诊断准确率最高提升了 13.7%。为评估语义有效性，视觉-语言模型从真实和合成图像生成的表型描述，其报告相似度评分达到 0.84。RDFace 建立了一个透明、即用型基准数据集，以促进公平的罕见病人工智能研究，并提出了一种可扩展的框架，用于评估诊断性能及合成医学图像的完整性。
URL
https://arxiv.org/abs/2604.03454
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Is Geometry Enough? An Evaluation of Landmark-Based Gaze Estimation
2026-03-25 18:51:09Daniele Agostinelli, Thomas Agostinelli, Andrea Generosi, Maura MengoniAbstract
Appearance-based gaze estimation frequently relies on deep Convolutional Neural Networks (CNNs). These models are accurate, but computationally expensive and act as "black boxes", offering little interpretability. Geometric methods based on facial landmarks are a lightweight alternative, but their performance limits and generalization capabilities remain underexplored in modern benchmarks. In this study, we conduct a comprehensive evaluation of landmark-based gaze estimation. We introduce a standardized pipeline to extract and normalize landmarks from three large-scale datasets (Gaze360, ETH-XGaze, and GazeGene) and train lightweight regression models, specifically Extreme Gradient Boosted trees and two neural architectures: a holistic Multi-Layer Perceptron (MLP) and a siamese MLP designed to capture binocular geometry. We find that landmark-based models exhibit lower performance in within-domain evaluation, likely due to noise introduced into the datasets by the landmark detector. Nevertheless, in cross-domain evaluation, the proposed MLP architectures show generalization capabilities comparable to those of ResNet18 baselines. These findings suggest that sparse geometric features encode sufficient information for robust gaze estimation, paving the way for efficient, interpretable, and privacy-friendly edge applications. The source code and generated landmark-based datasets are available at this https URL.
Abstract (translated)
基于外观的注视估计通常依赖于深度卷积神经网络（CNN）。这些模型虽然准确，但计算成本高昂且如同“黑箱模型”，可解释性不足。基于面部标志点的几何方法是一种轻量级替代方案，但其性能极限和泛化能力在现代基准测试中尚未得到充分探索。本研究对基于标志点的注视估计进行了全面评估。我们引入标准化流程，从三个大规模数据集（Gaze360、ETH-XGaze和GazeGene）中提取并归一化标志点，并训练轻量级回归模型，具体包括极端梯度提升树以及两种神经架构：整体式多层感知机（MLP）和用于捕捉双目几何特征的孪生MLP。研究发现，在域内评估中，基于标志点的模型表现较低，这可能源于标志点检测器引入的数据噪声。然而，在跨域评估中，所提出的MLP架构展现出与ResNet18基线相当的泛化能力。这些发现表明，稀疏几何特征编码了足够的信息以实现稳健的注视估计，为高效、可解释且隐私友好型的边缘应用铺平了道路。源代码及生成的基于标志点的数据集可通过此https URL获取。
URL
https://arxiv.org/abs/2603.24724
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Face anonymization preserving facial expressions and photometric realism
2026-03-18 10:16:12Luigi Celona, Simone Bianco, Raimondo SchettiniAbstract
The widespread sharing of face images on social media platforms and in large-scale datasets raises pressing privacy concerns, as biometric identifiers can be exploited without consent. Face anonymization seeks to generate realistic facial images that irreversibly conceal the subject's identity while preserving their usefulness for downstream tasks. However, most existing generative approaches focus on identity removal and image realism, often neglecting facial expressions as well as photometric consistency -- specifically attributes such as illumination and skin tone -- that are critical for applications like relighting, color constancy, and medical or affective analysis. In this work, we propose a feature-preserving anonymization framework that extends DeepPrivacy by incorporating dense facial landmarks to better retain expressions, and by introducing lightweight post-processing modules that ensure consistency in lighting direction and skin color. We further establish evaluation metrics specifically designed to quantify expression fidelity, lighting consistency, and color preservation, complementing standard measures of image realism, pose accuracy, and re-identification resistance. Experiments on the CelebA-HQ dataset demonstrate that our method produces anonymized faces with improved realism and significantly higher fidelity in expression, illumination, and skin tone compared to state-of-the-art baselines. These results underscore the importance of feature-aware anonymization as a step toward more useful, fair, and trustworthy privacy-preserving facial data.
Abstract (translated)
URL
https://arxiv.org/abs/2603.17567
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Deployment-Oriented Session-wise Meta-Calibration for Landmark-Based Webcam Gaze Tracking
2026-03-12 19:07:34Chenkai ZhangAbstract
Practical webcam gaze tracking is constrained not only by error, but also by calibration burden, robustness to head motion and session drift, runtime footprint, and browser use. We therefore target a deployment-oriented operating point rather than the image large-backbone regime. We cast landmark-based point-of-regard estimation as session-wise adaptation: a shared geometric encoder produces embeddings that can be aligned to a new session from a small calibration set. We present Equivariant Meta-Calibrated Gaze (EMC-Gaze), a lightweight landmark-only method combining an E(3)-equivariant landmark-graph encoder, local eye geometry, binocular emphasis, auxiliary 3D gaze-direction supervision, and a closed-form ridge calibrator differentiated through episodic meta-training. To reduce pose leakage, we use a two-view canonicalization consistency loss. The deployed predictor uses only facial landmarks and fits a per-session ridge head from brief calibration. In a fixation-style interactive evaluation over 33 sessions at 100 cm, EMC-Gaze achieves 5.79 +/- 1.81 deg RMSE after 9-point calibration versus 6.68 +/- 2.34 deg for Elastic Net; the gain is larger on still-head queries (2.92 +/- 0.75 deg vs. 4.45 +/- 0.30 deg). Across three subject holdouts of 10 subjects each, EMC-Gaze retains an advantage (5.66 +/- 0.19 deg vs. 6.49 +/- 0.33 deg). On MPIIFaceGaze with short per-session calibration, the eye-focused model reaches 8.82 +/- 1.21 deg at 16-shot calibration, ties Elastic Net at 1-shot, and outperforms it from 3-shot onward. The exported eye-focused encoder has 944,423 parameters, is 4.76 MB in ONNX, and supports calibrated browser prediction in 12.58/12.58/12.90 ms per sample (mean/median/p90) in Chromium 145 with ONNX Runtime Web. These results position EMC-Gaze as a calibration-friendly operating point rather than a universal state-of-the-art claim against heavier appearance-based systems.
Abstract (translated)
URL
https://arxiv.org/abs/2603.12388
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Morphology-Independent Facial Expression Imitation for Human-Face Robots
2026-03-07 06:57:52Xu Chen, Rui Gao, Che Sun, Zhehang Liu, Yuwei Wu, Shuo Yang, Yunde JiaAbstract
Accurate facial expression imitation on human-face robots is crucial for achieving natural human-robot interaction. Most existing methods have achieved photorealistic expression imitation through mapping 2D facial landmarks to a robot's actuator commands. Their imitation of landmark trajectories is susceptible to interference from facial morphology, which would lead to a performance drop. In this paper, we propose a morphology-independent expression imitation method that decouples expressions from facial morphology to eliminate morphological influence and produce more realistic expressions for human-face robots. Specifically, we construct an expression decoupling module to learn expression semantics by disentangling the expression representation from the morphology representation in a self-supervised manner. We devise an expression transfer module to map the representations to the robot's actuator commands through a learning objective of perceiving expression errors, producing accurate facial expressions based on the learned expression semantics. To support experimental validation, a custom-designed and highly expressive human-face robot, namely Pengrui, is developed to serve as an experimental platform for realistic expression imitation. Extensive experiments demonstrate that our method enables the human-face robot to reproduce a wide range of human-like expressions effectively. All code and implementation details of the robot will be released.
Abstract (translated)
在人形机器人的面部表情模仿中，准确地复制人类的面部表情对于实现自然的人机交互至关重要。现有的大多数方法通过将二维面部特征点映射到机器人的执行器命令来实现了逼真的表情模仿，但这些方法对脸部形态的变化较为敏感，这会导致表现效果下降。在本文中，我们提出了一种独立于形态的表情模仿方法，该方法能够分离出表情与面部形态，从而消除形态的影响，并产生更真实的人形机器人面部表情。 具体来说，我们构建了一个表情解耦模块，通过自监督学习来解析并学习表情语义，即从形态表示和表情表示中将其分开。我们设计了一个表情转移模块，它将这些表示映射到机器人的执行器命令上，以感知表情错误为目标进行训练，并根据学到的表情语义生成准确的面部表情。 为了支持实验验证，我们开发了一种定制且高度表达性的机器人——“彭睿”，作为用于真实表情模仿的实验平台。大量的实验证明了我们的方法能够让这种人形机器人有效而逼真地再现一系列人类面部表情。所有机器人的代码和实现细节都将公开发布。
URL
https://arxiv.org/abs/2603.07068
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All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity Watermark
2026-02-26 21:57:15Junjiang Wu, Liejun Wang, Zhiqing GuoAbstract
With the rapid advancement of deepfake technology, malicious face manipulations pose a significant threat to personal privacy and social security. However, existing proactive forensics methods typically treat deepfake detection, tampering localization, and source tracing as independent tasks, lacking a unified framework to address them jointly. To bridge this gap, we propose a unified proactive forensics framework that jointly addresses these three core tasks. Our core framework adopts an innovative 152-dimensional landmark-identity watermark termed LIDMark, which structurally interweaves facial landmarks with a unique source identifier. To robustly extract the LIDMark, we design a novel Factorized-Head Decoder (FHD). Its architecture factorizes the shared backbone features into two specialized heads (i.e., regression and classification), robustly reconstructing the embedded landmarks and identifier, respectively, even when subjected to severe distortion or tampering. This design realizes an "all-in-one" trifunctional forensic solution: the regression head underlies an "intrinsic-extrinsic" consistency check for detection and localization, while the classification head robustly decodes the source identifier for tracing. Extensive experiments show that the proposed LIDMark framework provides a unified, robust, and imperceptible solution for the detection, localization, and tracing of deepfake content. The code is available at this https URL.
Abstract (translated)
随着深度伪造技术的迅速发展，恶意的脸部篡改对个人隐私和社会安全构成了重大威胁。然而，现有的主动取证方法通常将深度伪造检测、篡改定位和源头追溯视为独立的任务，并缺乏一个统一框架来共同解决这些问题。为弥补这一空白，我们提出了一种统一的主动取证框架，该框架能够同时应对这三个核心任务。 我们的核心框架采用了一种创新性的152维地标-身份水印（LIDMark），这种水印将面部特征点与唯一来源标识符结构化地交织在一起。为了稳健地提取LIDMark，我们设计了一个新颖的因子头部解码器（Factorized-Head Decoder, FHD）。FHD架构将其共享骨干特征分解为两个专门化的头部（即回归和分类），即使在遭受严重扭曲或篡改的情况下，也能分别稳健地重构嵌入的地标和标识符。这种设计实现了“三位一体”的多功能取证解决方案：回归头部用于深度伪造检测和定位的内在-外在一致性检查，而分类头部则通过解码来源标识符来进行稳健的溯源。 广泛的实验表明，所提出的LIDMark框架为深度伪造内容的检测、定位和追踪提供了一种统一、鲁棒且不可感知的解决方案。代码可在[此处](https://这个URL)获取。
URL
https://arxiv.org/abs/2602.23523
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Locally Adaptive Decay Surfaces for High-Speed Face and Landmark Detection with Event Cameras
2026-02-26 15:16:04Paul Kielty, Timothy Hanley, Peter CorcoranAbstract
Event cameras record luminance changes with microsecond resolution, but converting their sparse, asynchronous output into dense tensors that neural networks can exploit remains a core challenge. Conventional histograms or globally-decayed time-surface representations apply fixed temporal parameters across the entire image plane, which in practice creates a trade-off between preserving spatial structure during still periods and retaining sharp edges during rapid motion. We introduce Locally Adaptive Decay Surfaces (LADS), a family of event representations in which the temporal decay at each location is modulated according to local signal dynamics. Three strategies are explored, based on event rate, Laplacian-of-Gaussian response, and high-frequency spectral energy. These adaptive schemes preserve detail in quiescent regions while reducing blur in regions of dense activity. Extensive experiments on the public data show that LADS consistently improves both face detection and facial landmark accuracy compared to standard non-adaptive representations. At 30 Hz, LADS achieves higher detection accuracy and lower landmark error than either baseline, and at 240 Hz it mitigates the accuracy decline typically observed at higher frequencies, sustaining 2.44 % normalized mean error for landmarks and 0.966 mAP50 in face detection. These high-frequency results even surpass the accuracy reported in prior works operating at 30 Hz, setting new benchmarks for event-based face analysis. Moreover, by preserving spatial structure at the representation stage, LADS supports the use of much lighter network architectures while still retaining real-time performance. These results highlight the importance of context-aware temporal integration for neuromorphic vision and point toward real-time, high-frequency human-computer interaction systems that exploit the unique advantages of event cameras.
Abstract (translated)
事件相机以微秒级分辨率记录亮度变化，但将这些稀疏、异步的输出转换成神经网络可以利用的密集张量仍然是一个核心挑战。传统的直方图或全局衰减时间表面表示方法在整个图像平面上应用固定的时序参数，在实践中造成了在静止期间保持空间结构与快速运动区域保留锐利边缘之间的权衡。我们引入了局部自适应衰减表面（LADS），这是一个事件表示家族，其中每个位置的时序衰减根据本地信号动态进行调节。三种策略被探索，分别基于事件速率、高斯拉普拉斯响应和高频频谱能量。这些自适应方案在静止区域保留细节的同时减少了密集活动区域的模糊度。公开数据集上的广泛实验表明，LADS与标准非自适应表示相比，在面部检测和面部标志准确性方面均有所提升。在30 Hz下，LADS实现了更高的检测准确性和更低的地标误差，而在240 Hz时则缓解了高频操作通常观察到的精度下降问题，维持了2.44%的标准化平均错误率用于地标识别以及0.966 mAP50用于面部检测。这些高频结果甚至超过了先前在30 Hz运行的工作所报告的准确性，在基于事件的脸部分析中建立了新的基准。此外，通过在表示阶段保留空间结构，LADS支持使用更轻量级的网络架构同时保持实时性能。这些结果突显了神经形态视觉中上下文感知时序整合的重要性，并指向能够利用事件相机独特优势的实时高频人机交互系统的未来方向。
URL
https://arxiv.org/abs/2602.23101
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A Novel Evolutionary Method for Automated Skull-Face Overlay in Computer-Aided Craniofacial Superimposition
2026-02-26 13:55:19Pr\'axedes Mart\'inez-Moreno, Andrea Valsecchi, Pablo Mesejo, Pilar Navarro-Ram\'irez, Valentino Lugli, Sergio DamasAbstract
Craniofacial Superimposition is a forensic technique for identifying skeletal remains by comparing a post-mortem skull with ante-mortem facial photographs. A critical step in this process is Skull-Face Overlay (SFO). This stage involves aligning a 3D skull model with a 2D facial image, typically guided by cranial and facial landmarks' correspondence. However, its accuracy is undermined by individual variability in soft-tissue thickness, introducing significant uncertainty into the overlay. This paper introduces Lilium, an automated evolutionary method to enhance the accuracy and robustness of SFO. Lilium explicitly models soft-tissue variability using a 3D cone-based representation whose parameters are optimized via a Differential Evolution algorithm. The method enforces anatomical, morphological, and photographic plausibility through a combination of constraints: landmark matching, camera parameter consistency, head pose alignment, skull containment within facial boundaries, and region parallelism. This emulation of the usual forensic practitioners' approach leads Lilium to outperform the state-of-the-art method in terms of both accuracy and robustness.
Abstract (translated)
颅面叠合技术是一种法医手段，通过比较死后的头骨与生前的面部照片来识别骨骼遗骸。在这个过程中最关键的步骤是颅面重叠（Skull-Face Overlay, SFO）。该阶段涉及将一个3D头骨模型与2D面部图像对齐，通常依据颅部和面部标志点的一致性来进行指导。然而，这种方法的准确性因个体软组织厚度的变化而受到影响，这引入了显著的不确定性。 本文介绍了Lilium这一自动化进化方法，旨在提高SFO的准确性和鲁棒性。Lilium通过使用基于3D锥体表示的模型来显式地建模软组织变化，并利用差分演化算法优化其参数。该方法通过一系列约束条件确保解剖学、形态学和摄影上的合理性：包括标志点匹配、相机参数一致性、头部姿态对齐、头骨位于面部边界内以及区域平行性。这种模仿传统法医从业者的方法使Lilium在准确性和鲁棒性方面都超越了当前最先进的方法。 简而言之，通过更精确地模拟软组织的变化，并优化模型的参数，Lilium能够提供比现有技术更加可靠和精准的结果。
URL
https://arxiv.org/abs/2603.00170
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