Face Alignment by Explicit Shape Regression

Cao, Xudong; Wei, Yichen; Wen, Fang; Sun, Jian

doi:10.1007/s11263-013-0667-3

Cited by 883 publications

(994 citation statements)

References 22 publications

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“…5, there is a long shadow on a face with a flat nose. Since this problem is mostly due to inaccurate face alignment, we could take more facial landmarks in a significant region or employ multi-pose face alignment [19] to improve the performance.…”

Section: Resultsmentioning

confidence: 99%

A New Face Relighting Method Based on Edge-Preserving Filter

Liang

Jin

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe propose a new face relighting method using an illuminance template generated from a single reference portrait. First, the reference is wrapped according to the shape of the target. Second, we employ a new spatially variant edge-preserving smoothing filter to remove the facial identity and texture details of the wrapped reference, and obtain the illumination template. Finally, we relight the target with the template in CIELAB color space. Experiments show the effectiveness of our method for both grayscale and color faces taken from different databases, and the comparisons with previous works demonstrate a better relighting effect produced by our method.

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Section: Resultsmentioning

confidence: 99%

A New Face Relighting Method Based on Edge-Preserving Filter

Liang

Jin

2013

IEICE Trans. Inf. & Syst.

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“…ESR [10] uses shape indexed intensity difference features for face alignment based on CPR [9]. Moreover, SDM extracts shape-indexed SIFT features and learns a sequence of general descent maps from supervised training data, providing a solution when Newton Descent method is hard to be utilized for a not analytically differentiable nonlinear function or Hessian matrix is too large and not positive definite.…”

Section: Cascaded Regression To Face Alignmentmentioning

confidence: 99%

“…We evaluate the proposed sign-correlation partition SDM method on the challenging 300W dataset, and compare it with state-of-the-art methods ESR [10], SDM [11], ERT [32], and LBF [33]. As mentioned above, there are 68 labeled landmarks in this dataset.…”

Section: Comparison Of Face Alignmentmentioning

confidence: 99%

“…The main difference between cascaded regression and related boosting regression is that cascaded regression uses shape-indexed features extracted from the image according to the current estimated shape. Cascaded Pose Regression (CPR) [9] for pose estimation has been widely extended into face alignment in current works, represented by Explicit Shape Regression (ESR) [10], and Supervised Descent Method (SDM) [11]. It is noticed that SDM provides a theoretical explanation of the cascaded regression from the point of view of optimizing a nonlinear problem, as a significant achievement in cascaded regression-based methods.…”

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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“…There have been many recent achievements in related research sub-areas such as facial landmark localization [3][4] [5][6] [7] [8], tracking and recognition [9] [10]. Realistic facial expression synthesis is useful for affective computing, human computer interaction [11] [12], realistic computer animation [13] and facial surgery planning [14][15], etc.…”

Section: Introductionmentioning

confidence: 99%

Facial expression transfer method based on frequency analysis

Wei

Tian

Maybank

et al. 2016

Pattern Recognition

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Abstract. We propose a novel expression transfer method based on an analysis of the frequency of multi-expression facial images. We locate the facial features automatically and describe the shape deformations between a neutral expression and non-neutral expressions. The subtle expression changes are important visual clues to distinguish different expressions. These changes are more salient in the frequency domain than in the image domain. We extract the subtle local expression deformations for the source subject, coded in the wavelet decomposition. This information about expressions is transferred to a target subject. The resulting synthesized image preserves both the facial appearance of the target subject and the expression details of the source subject.This method is extended to dynamic expression transfer to allow a more precise interpretation of facial expressions.Experiments on Japanese Female Facial Expression (JAFFE), the extended Cohn-Kanade (CK+) and PIE facial expression databases show the superiority of our method over the state-of-the-art method.

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Face Alignment by Explicit Shape Regression

Cited by 883 publications

References 22 publications

A New Face Relighting Method Based on Edge-Preserving Filter

A New Face Relighting Method Based on Edge-Preserving Filter

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

Facial expression transfer method based on frequency analysis

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