Look at Boundary: A Boundary-Aware Face Alignment Algorithm

Published in

(2018)

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TL;DR

A novel boundary-aware face alignment algorithm by utilising boundary lines as the geometric structure of a human face to help facial landmark localisation, which outperforms state-of-the-art methods by a large margin.

Abstract

We present a novel boundary-aware face alignment algorithm by utilising boundary lines as the geometric structure of a human face to help facial landmark localisation. Unlike the conventional heatmap based method and regression based method, our approach derives face landmarks from boundary lines which remove the ambiguities in the landmark definition. Three questions are explored and answered by this work: 1. Why using boundary? 2. How to use boundary? 3. What is the relationship between boundary estimation and landmarks localisation? Our boundary-aware face alignment algorithm achieves 3.49% mean error on 300-W Fullset, which outperforms state-of-the-art methods by a large margin. Our method can also easily integrate information from other datasets. By utilising boundary information of 300-W dataset, our method achieves 3.92% mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training and testing across different factors, including poses, expressions, illuminations, makeups, occlusions, and blurriness. Dataset and model are publicly available at https://wywu.github.io/projects/LAB/LAB.html

Authors

Shuo Yang

3 papers

Wayne Wu

14 papers

C. Qian

8 papers

References76 items

Style Aggregated Network for Facial Landmark Detection

Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment

Joint Multi-View Face Alignment in the Wild

Learning Feature Pyramids for Human Pose Estimation

A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection

Look at Boundary: A Boundary-Aware Face Alignment Algorithm

Published in

(2018)

External Links:

Generate Graph DownloadPDF

TL;DR

Abstract

Authors

Shuo Yang

3 papers

Wayne Wu

14 papers

C. Qian

8 papers

References76 items

Style Aggregated Network for Facial Landmark Detection

Disentangling 3D Pose in a Dendritic CNN for Unconstrained 2D Face Alignment

Joint Multi-View Face Alignment in the Wild

Learning Feature Pyramids for Human Pose Estimation

A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection

Self Adversarial Training for Human Pose Estimation

Simultaneous Facial Landmark Detection, Pose and Deformation Estimation Under Facial Occlusion

Leveraging Intra and Inter-Dataset Variations for Robust Face Alignment

Stacked Hourglass Network for Robust Facial Landmark Localisation

The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution

CASENet: Deep Category-Aware Semantic Edge Detection

Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation

Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection

Multi-context Attention for Human Pose Estimation

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

Boundary-Aware Instance Segmentation

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DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild

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Dynamic Attention-Controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-Set Sample Weighting

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Proceedings of the British Machine Vision Conference, BMVC 1992, Leeds, UK, September, 1992

Field of Study

Computer Science

Journal Information

Name

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Page

2129-2138