Facial Landmark Detection

47 papers with code • 9 benchmarks • 15 datasets

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 )

Libraries

Use these libraries to find Facial Landmark Detection models and implementations
2 papers
4,884

Most implemented papers

Deep Neural Networks Regularization for Structured Output Prediction

sbelharbi/structured-output-ae 28 Apr 2015

The motivation of this work is to learn the output dependencies that may lie in the output data in order to improve the prediction accuracy.

A Multiresolution 3D Morphable Face Model and Fitting Framework

patrikhuber/eos 1 Feb 2016

In this paper, we present the Surrey Face Model, a multi-resolution 3D Morphable Model that we make available to the public for non-commercial purposes.

Convolutional Experts Constrained Local Model for Facial Landmark Detection

TadasBaltrusaitis/OpenFace 26 Nov 2016

In our work, we present a novel local detector -- Convolutional Experts Network (CEN) -- that brings together the advantages of neural architectures and mixtures of experts in an end-to-end framework.

Combining Data-driven and Model-driven Methods for Robust Facial Landmark Detection

HongwenZhang/ECT-FaceAlignment 30 Nov 2016

This Estimation-Correction-Tuning process perfectly combines the advantages of the global robustness of data-driven method (FCN), outlier correction capability of model-driven method (PDM) and non-parametric optimization of RLMS.

A Deep Regression Architecture With Two-Stage Re-Initialization for High Performance Facial Landmark Detection

shaoxiaohu/Face_Alignment_Two_Stage_Re-initialization CVPR 2017

At the global stage, given an image with a rough face detection result, the full face region is firstly re-initialized by a supervised spatial transformer network to a canonical shape state and then trained to regress a coarse landmark estimation.

Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container

natanielruiz/dockerface 15 Aug 2017

Face detection is a very important task and a necessary pre-processing step for many applications such as facial landmark detection, pose estimation, sentiment analysis and face recognition.

Dense Face Alignment

yaojieliu/ICCVW2017-DenseFaceAlignment 5 Sep 2017

Face alignment is a classic problem in the computer vision field.

Multi-Task Learning by Deep Collaboration and Application in Facial Landmark Detection

ltrottier/deep-collaboration-network ICLR 2018

We show that CNNs connected with our Deep Collaboration obtain better accuracy on facial landmark detection with related tasks.

ExpNet: Landmark-Free, Deep, 3D Facial Expressions

fengju514/Expression-Net 2 Feb 2018

Our ExpNet CNN is applied directly to the intensities of a face image and regresses a 29D vector of 3D expression coefficients.

Style Aggregated Network for Facial Landmark Detection

D-X-Y/SAN CVPR 2018

In this work, we propose a style-aggregated approach to deal with the large intrinsic variance of image styles for facial landmark detection.