Facial point detection using boosted regression and graph models
Michel Valstar,Brais Martinez,Xavier Binefa,Maja Pantic +3 more
- pp 2729-2736
TLDR
A method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point's location and increase the accuracy and robustness of the algorithm.Abstract:
Finding fiducial facial points in any frame of a video showing rich naturalistic facial behaviour is an unsolved problem. Yet this is a crucial step for geometric-feature-based facial expression analysis, and methods that use appearance-based features extracted at fiducial facial point locations. In this paper we present a method based on a combination of Support Vector Regression and Markov Random Fields to drastically reduce the time needed to search for a point's location and increase the accuracy and robustness of the algorithm. Using Markov Random Fields allows us to constrain the search space by exploiting the constellations that facial points can form. The regressors on the other hand learn a mapping between the appearance of the area surrounding a point and the positions of these points, which makes detection of the points very fast and can make the algorithm robust to variations of appearance due to facial expression and moderate changes in head pose. The proposed point detection algorithm was tested on 1855 images, the results of which showed we outperform current state of the art point detectors.read more
Citations
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Proceedings ArticleDOI
Deep Convolutional Network Cascade for Facial Point Detection
Yi Sun,Xiaogang Wang,Xiaoou Tang +2 more
TL;DR: The proposed approach outperforms state-of-the-art methods in both detection accuracy and reliability and can avoid local minimum caused by ambiguity and data corruption in difficult image samples due to occlusions, large pose variations, and extreme lightings.
Book ChapterDOI
Facial Landmark Detection by Deep Multi-task Learning
TL;DR: A novel tasks-constrained deep model is formulated, with task-wise early stopping to facilitate learning convergence and reduces model complexity drastically compared to the state-of-the-art method based on cascaded deep model.
Journal ArticleDOI
Face Alignment by Explicit Shape Regression
TL;DR: A very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment that significantly outperforms the state-of-the-art in terms of both accuracy and efficiency.
Proceedings ArticleDOI
Face Alignment Across Large Poses: A 3D Solution
TL;DR: 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN), is proposed, and a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling is proposed.
Proceedings ArticleDOI
Face Alignment at 3000 FPS via Regressing Local Binary Features
TL;DR: This paper presents a highly efficient, very accurate regression approach for face alignment that achieves the state-of-the-art results when tested on the current most challenging benchmarks.
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