Robust Face Landmark Estimation under Occlusion

Burgos-Artizzu, Xavier P.; Perona, Pietro; Dollár, Piotr

doi:10.1109/iccv.2013.191

Cited by 653 publications

(663 citation statements)

References 41 publications

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“…Burgos-Artizzu et al [12] integrate part visibility term into landmarks and presents interpolated shape-indexed features to tackle with occlusions and high shape variances. Kazemi et al [32] estimate facial landmarks by learning an ensemble of regression trees (ERT) directly from a sparse subset of pixel intensities.…”

Section: Cascaded Regression To Face Alignmentmentioning

confidence: 99%

“…Some methods among them have begun to deal with the impact of poses, like RPCR [12], SRD [13], CCR [31], hierarchical localization [34] and coarse-tofine searching [30]. However, few of them can give a clear interpretation for the correlation between poses and feature or shapes.…”

Section: Multi-pose Face Alignmentmentioning

confidence: 99%

“…Some of them can handle partial occlusions [12][13][14]. Some works mainly aim at speeding up the prediction process while keeping high accuracy [13,15].…”

Section: Introductionmentioning

confidence: 99%

“…Though their scheme works well for face tracking and pose estimation, it is not suitable for face alignment across various poses, because the ground truth of shapes or features are unknown and it is not able to find approximation due to lack of previous frame. A few recent works [12,13,[28][29][30][31] begin to consider the influence of multiple poses. Most of them deal with the problem indirectly by random schemes or data augment, and they can only handle small changes in poses.…”

Section: Introductionmentioning

confidence: 99%

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Sign-Correlation Partition Based on Global Supervised Descent Method for Face Alignment

Zhang

Liu

Yang

et al. 2017

Lecture Notes in Computer Science

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Abstract. Face alignment is an essential task for facial performance capture and expression analysis. As a complex nonlinear problem in computer vision, face alignment across poses is still not studied well. Although the state-of-the-art Supervised Descent Method (SDM) has shown good performance, it learns conflict descent direction in the whole complex space due to various poses and expressions. Global SDM has been presented to deal with this case by domain partition in feature and shape PCA spaces for face tracking and pose estimation. However, it is not suitable for the face alignment problem due to unknown ground truth shapes. In this paper we propose a sign-correlation subspace method for the domain partition of global SDM. In our method only one reduced low dimensional subspace is enough for domain partition, thus adjusting the global SDM efficiently for face alignment. Unlike previous methods, we analyze the sign correlation between features and shapes, and project both of them into a mutual sign-correlation subspace. Each pair of projected shape and feature keep sign consistent in each dimension of the subspace, so that each hyperoctant holds the condition that one general descent exists. Then a set of general descent directions are learned from the samples in different hyperoctants. Our sign-correlation partition method is validated in the public face datasets, which includes a range of poses. It indicates that our methods can reveal their latent relationships to poses. The comparison with state-of-the-art methods for face alignment demonstrates that our method outperforms them especially in uncontrolled conditions with various poses, while keeping comparable speed.

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Section: Cascaded Regression To Face Alignmentmentioning

confidence: 99%

Section: Multi-pose Face Alignmentmentioning

confidence: 99%

“…Some of them can handle partial occlusions [12][13][14]. Some works mainly aim at speeding up the prediction process while keeping high accuracy [13,15].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sign-Correlation Partition Based on Global Supervised Descent Method for Face Alignment

Zhang

Liu

Yang

et al. 2017

Lecture Notes in Computer Science

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show abstract

“…These two problems have been separately studied for many years [3][4][5][6][7], with significant progress for images [8][9][10][11]. However, image-based methods are always subject to illumination and pose angle variations [12], which lead to many limitations.…”

Section: Introductionmentioning

confidence: 99%

Joint head pose and facial landmark regression from depth images

et al. 2017

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This paper presents a joint head pose and facial landmark regression method with input from depth images for realtime application. Our main contributions are: firstly, a joint optimization method to estimate head pose and facial landmarks, i.e., the pose regression result provides supervised initialization for cascaded facial landmark regression, while the regression result for the facial landmarks can also help to further refine the head pose at each stage. Secondly, we classify the head pose space into 9 sub-spaces, and then use a cascaded random forest with a global shape constraint for training facial landmarks in each specific space. This classification-guided method can effectively handle the problem of large pose changes and occlusion. Lastly, we have built a 3D face database containing 73 subjects, each with 14 expressions in various head poses. Experiments on challenging databases show our method achieves state-of-the-art performance on both head pose estimation and facial landmark regression.

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