Browse State-of-the-Art › Facial Landmark Detection
Facial Landmark Detection
52 papers with code · 10 benchmarks · 16 datasets archive 2025-07-28
Facial Landmark Detection is a computer vision task that involves detecting and localizing specific points or landmarks on a face, such as the eyes, nose, mouth, and chin. The goal is to accurately identify these landmarks in images or videos of faces in real-time and use them for various applications, such as face recognition, facial expression analysis, and head pose estimation.
( Image credit: Style Aggregated Network for Facial Landmark Detection )
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
10 leaderboard tables shown for this task, 10 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
16 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
3 subtasks in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
30 shown of 52 papers with code (139 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
-
9 Apr 2019 39 repositories listed Syntology ran 3 of 18 samples · 15 unverified · 5 pointer-only (licence)The proposed approach achieves superior results to existing single-model networks on COCO object detection.
-
28 Feb 2019 18 repositories listedBeing accurate, efficient, and compact is essential to a facial landmark detector for practical use.
-
24 Aug 2017 5 repositories listedInstead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
-
26 Mar 2014 3 repositories listedWe describe a method that can accurately estimate the positions of relevant facial landmarks in real-time even on hardware with limited processing power, such as mobile devices.
-
19 Mar 2024 2 repositories listedIn this work, we introduce FaceXFormer, an end-to-end unified transformer model capable of performing nine facial analysis tasks including face parsing, landmark detection, head pose estimation, attribute prediction,…
-
14 Dec 2020 2 repositories listedTests on AFLW2000-3D and BIWI show that our method runs at real-time and outperforms state of the art (SotA) face pose estimators.
-
23 Jul 2020 2 repositories listedThis paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
-
8 Mar 2020 2 repositories listed Syntology ran 2 of 17 samples · 15 unverifiedThe proposed model is equipped with a novel detection head based on heatmap regression, which conducts score and offset predictions simultaneously on low-resolution feature maps.
-
22 Dec 2019 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization…
-
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection6 Aug 2019 2 repositories listed Syntology ran 1 of 9 samples · 8 unverifiedA typical approach is to (1) train a detector on the labeled images; (2) generate new training samples using this detector's prediction as pseudo labels of unlabeled images; (3) retrain the detector on the labeled…
-
9 Feb 2019 2 repositories listedWe present a method for highly efficient landmark detection that combines deep convolutional neural networks with well established model-based fitting algorithms.
-
26 May 2018 2 repositories listedBy utilising boundary information of 300-W dataset, our method achieves 3.
-
17 Jun 2025 1 repository listedIn particular, we propose a novel F5C block composed of fully-connected convolution and channel correspondence convolution to directly extract local-global features from a sequence of raw frames, without the prior…
-
1 Dec 2024 1 repository listedFinally, by integrating the DSA model and DSS model into our proposed DSAT in both dynamic architecture and dynamic parameter manners, more specialized features can be learned for achieving more precise face alignment.
-
8 Nov 2024 1 repository listedThis paper introduces a new facial landmark detector based on vision transformers, which consists of two unique designs: Dual Vision Transformer (D-ViT) and Long Skip Connections (LSC).
-
12 Oct 2024 1 repository listed(2) To enhance the pseudo-range accuracy of selected anchor points, a new loss function, named multilateration anchor loss, is proposed.
-
24 Jan 2024 1 repository listedFinally, we fine-tuned a pre-trained face landmark detection model on the synthetic dataset to achieve multi-domain face landmark detection.
-
5 Jun 2023 1 repository listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)To solve this problem, we propose a Self-adapTive Ambiguity Reduction (STAR) loss by exploiting the properties of semantic ambiguity.
-
25 May 2023 1 repository listedBy spearheading the integration of Multilateration with facial analysis, KeyPosS marks a paradigm shift in facial landmark detection.
-
17 Oct 2022 1 repository listedFacial landmark detection plays an important role for the similarity analysis in artworks to compare portraits of the same or similar artists.
-
13 Oct 2022 1 repository listedTop-performing landmark estimation algorithms are based on exploiting the excellent ability of large convolutional neural networks (CNNs) to represent local appearance.
-
29 Mar 2022 1 repository listedHeatmap-based Regression (HBR) and Coordinate-based Regression (CBR) are among the two mainly used methods for face alignment.
-
13 Nov 2021 1 repository listedWe use two Teacher networks, a Tolerant-Teacher and a Tough-Teacher in conjunction with the Student network.
-
28 Aug 2021 1 repository listedTo encounter this problem, we made the best use of GAN-based data augmentation to generate extra dataset instances.
-
8 Jun 2021 1 repository listedDifferently, in whole-body pose estimation, the locations of fine-grained keypoints (68 on face, 21 on each hand and 3 on each foot) are estimated as well, which creates a scale variance problem that needs to be…
-
1 Jan 2021 1 repository listedWe argue that exploring the weaknesses of the detector so as to remedy them is a promising method of robust facial landmark detection.
-
12 Dec 2020 1 repository listedThe radical student uses multi-source supervision signals from the same task to update parameters, while the calm teacher uses a single-source supervision signal to update parameters.
-
9 Dec 2020 1 repository listedRecently, heatmap regression has been widely explored in facial landmark detection and obtained remarkable performance.
-
18 Oct 2020 1 repository listedExisting deep learning based facial landmark detection methods have achieved excellent performance.
-
7 Jul 2020 1 repository listedBased on the prediction of each anchor template, we propose to aggregate the results, which can reduce the landmark uncertainty due to the large poses.
Syntology lines on 5 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections