Facial Landmark Localization

Latest papers

8 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

RCFormer: Reliability-aware Context Transformer for Occlusion-Robust Facial Landmark Detection
Nov 1, 2026Ashish Kumar, Prashant Kumar, Mallika GargFacial Landmark Localization Anatomical Landmark Detection
Indian Institute of Technology Roorkee, Roorkee, INDIA · Afinit Co. Ltd, Seoul, Republic of Korea · Indian Institute of Technology Kharagpur, Kharagpur, INDIA
A Multi-Task Deep Learning Framework for Pose-Invariant Face Recognition and Facial Landmark Localization
Oct 12, 2026Ali Ashkeyev, Zukhra AbdiakhmetovaFacial Landmark Localization Face Recognition
Faculty of Information Technology, Kazakh-British Technical University, Almaty, Kazakhstan · Faculty of Information Technology and Artificial Intelligence, Abai Kazakh National Pedagogical University, Almaty, Kazakhstan
FAHCD-Net: Frequency-Adaptive Heatmap-Conditional Diffusion Networks for Robust Facial Landmark Detection
Sep 15, 2026Jun Wan, Jiwei Hu, Shengkai Hu +1Facial Landmark Localization Frequency-Domain Feature Learning
School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China
FreqFLD: Towards All-in-One Facial Landmark Detection via Frequency Modulation
Sep 9, 2026Shun Ren, Kaijie Jin, Shengkai Hu +5Facial Landmark Localization Mixture of Experts
College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China · School of Information Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China · National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China +3
Towards Unified Dynamic Face Landmark Detection
Aug 11, 2026Sebastian Regalado, Varshanth R. Rao, Ruowei Jiang +2Facial Landmark Localization
University of Toronto · 2ModiFace · †Work completed while employed at ModiFace +1
NeoLoc-68: End-to-end 68-point neonatal facial landmark localisation in neonatal clinical environments
Jun 18, 2026Abdullah Bin-Obaid, Maria M. Cobo, Rebeccah Slater +2Healthcare Facial Landmark Localization
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom · Department of Paediatrics, University of Oxford, Oxford, United Kingdom · Universidad San Francisco de Quito USFQ, Colegio de Ciencias Biologicas y Ambientales, Quito, Ecuador
WorldComp2D: Spatio-semantic Representations of Object Identity and Location from Local Views
May 12, 2026SeongMin Jin, Doo Seok JeongRepresentation Learning Visual Representation Learning
1Division of Materials Science and Engineering · Department of Semiconductor Engineering
Auditing Demographic Bias in Facial Landmark Detection for Fair Human-Robot Interaction
Apr 8, 2026Pablo Parte, Roberto Valle, José M. Buenaposada +1Facial Landmark Localization Algorithmic Fairness
Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, Spain · Departamento de Informática y Estadística, Universidad Rey Juan Carlos, Spain

Nov 1, 2026J/K move · Enter open · S save

RCFormer: Reliability-aware Context Transformer for Occlusion-Robust Facial Landmark Detection

Ashish Kumar, Prashant Kumar, Mallika Garg

Indian Institute of Technology Roorkee, Roorkee, INDIA · Afinit Co. Ltd, Seoul, Republic of Korea · Indian Institute of Technology Kharagpur, Kharagpur, INDIA

Facial Landmark Localization Anatomical Landmark Detection Transformer

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Facial landmark detection has advanced substantially under regular imaging conditions, yet occlusion, poor illumination, and large pose changes remain major sources of error because local facial evidence can become incomplete or misleading. Context-guided latent patch recovery offers a promising way to address this issue by inferring unreliable patch representations from surrounding facial context before heatmap prediction. However, the recovered context can itself be unreliable when it is weakly adapted to the input face, influenced by corrupted neighboring patches, or blended without preserving local spatial coherence. We propose RCFormer, a Reliability-aware Context Transformer for occlusion-robust facial landmark detection. RCFormer strengthens latent patch recovery with three reliability-oriented components: visible-context conditioning (VCC), which adapts the context representation using a visibility-weighted global facial summary; visibility-guided context aggregation (VGCA), which reduces the influence of unreliable source patches during attention; and spatial residual correction (SRC), which refines blended latent features using neighboring patch information. The recovered heatmaps are then used as reliability-aware structural cues for existing landmark detection networks. Experiments show that RCFormer improves heatmap recovery and achieves strong landmark localization results on the WFLW, COFW, and 300W datasets.