Fast, robust and automatic 3D face model reconstruction from videos

Qu, Chengchao; Monari, Eduardo; Schuchert, Tobias; Beyerer, Jürgen

doi:10.1109/avss.2014.6918653

Cited by 18 publications

(9 citation statements)

References 23 publications

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“…This is because the position of facial contour in the 2D image changes along with pose variations. To handle this problem, (Lee et al 2012;Qu et al 2014) proposed to detect and discard the moved landmarks. Asthana et al (2011b) proposed the construction of a lookup table which contains the manually labeled 3D vertices that correspond to the 2D facial landmarks under a set of discrete poses.…”

Section: Normalization Using Pca-based Face Modelsmentioning

confidence: 99%

A Comprehensive Survey on Pose-Invariant Face Recognition

Ding

Tao

2016

ACM Trans. Intell. Syst. Technol.

254

137

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The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, pose-invariant face recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this paper, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, i.e., pose-robust feature extraction approaches, multi-view subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed.

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Section: Normalization Using Pca-based Face Modelsmentioning

confidence: 99%

A Comprehensive Survey on Pose-Invariant Face Recognition

Ding

Tao

2016

ACM Trans. Intell. Syst. Technol.

254

137

View full text Add to dashboard Cite

show abstract

“…However, they still suffer from invisible facial landmarks when the input face has large pose angles. To deal with extreme poses, Lee et al [39], Qu et al [40] and Liu et al [41] propose to discard the selfoccluded landmarks or treat them as missing data.…”

Section: D Face Reconstructionmentioning

confidence: 99%

Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition

Liu

Zhao

Liu

et al. 2020

IEEE Trans. Pattern Anal. Mach. Intell.

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Face alignment and 3D face reconstruction are traditionally accomplished as separated tasks. By exploring the strong correlation between 2D landmarks and 3D shapes, in contrast, we propose a joint face alignment and 3D face reconstruction method to simultaneously solve these two problems for 2D face images of arbitrary poses and expressions. This method, based on a summation model of 3D faces and cascaded regression in 2D and 3D shape spaces, iteratively and alternately applies two cascaded regressors, one for updating 2D landmarks and the other for 3D shape. The 3D shape and the landmarks are correlated via a 3D-to-2D mapping matrix, which is updated in each iteration to refine the location and visibility of 2D landmarks. Unlike existing methods, the proposed method can fully automatically generate both pose-and-expression-normalized (PEN) and expressive 3D faces and localize both visible and invisible 2D landmarks. Based on the PEN 3D faces, we devise a method to enhance face recognition accuracy across poses and expressions. Both linear and nonlinear implementations of the proposed method are presented and evaluated in this paper. Extensive experiments show that the proposed method can achieve the state-of-the-art accuracy in both face alignment and 3D face reconstruction, and benefit face recognition owing to its reconstructed PEN 3D face.

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“…2 for example. To solve the problem, Lee et al [31] and Qu et al [40] detect and discard moved landmarks. This method cannot make full use of landmark constrains.…”

Section: Landmark Marchingmentioning

confidence: 99%

High-fidelity Pose and Expression Normalization for face recognition in the wild

Zhu

Lei

Yan

et al. 2015

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

137

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Pose and expression normalization is a crucial step to recover the canonical view of faces under arbitrary conditions, so as to improve the face recognition performance. An ideal normalization method is desired to be automatic, database independent and high-fidelity, where the face appearance should be preserved with little artifact and information loss. However, most normalization methods fail to satisfy one or more of the goals. In this paper, we propose a High-fidelity Pose and Expression Normalization (HPEN) method with 3D Morphable Model (3DMM) which can automatically generate a natural face image in frontal pose and neutral expression. Specifically, we firstly make a landmark marching assumption to describe the non-correspondence between 2D and 3D landmarks caused by pose variations and propose a pose adaptive 3DMM fitting algorithm. Secondly, we mesh the whole image into a 3D object and eliminate the pose and expression variations using an identity preserving 3D transformation. Finally, we propose an inpainting method based on Possion Editing to fill the invisible region caused by self occlusion. Extensive experiments on Multi-PIE and LFW demonstrate that the proposed method significantly improves face recognition performance and outperforms state-of-the-art methods in both constrained and unconstrained environments.

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Fast, robust and automatic 3D face model reconstruction from videos

Cited by 18 publications

References 23 publications

A Comprehensive Survey on Pose-Invariant Face Recognition

A Comprehensive Survey on Pose-Invariant Face Recognition

Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition

High-fidelity Pose and Expression Normalization for face recognition in the wild

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