Enhancing the Symmetry and Proportion of 3D Face Geometry

Liao, Qiqi; Jin, Xiaogang; Zeng, Wenting

doi:10.1109/tvcg.2012.26

Cited by 37 publications

(30 citation statements)

References 24 publications

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“…Marcolin et al [32] also devised facial geometrical descriptors representing symmetry features for the attractiveness analysis of 3D faces. Liao et al [33] manually selected landmarks for the golden ratio, neoclassical canon, and symmetry on a 3D face, and these distances and ratio values were revised by considering the surface characteristics of a 3D face. Table 1 summarizes the related studies.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Framework for Assessing Facial Attractiveness Based on Facial Proportions

et al. 2017

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Abstract:In this paper, we present a novel framework for automatically assessing facial attractiveness that considers four ratio feature sets as objective elements of facial attractiveness. In our framework, these feature sets are combined with three regression-based predictors to estimate a facial beauty score. To enhance the system's performance to make it comparable with human scoring, we apply a score fusion technique. Experimental results show that the attractiveness score obtained by the proposed framework better correlates with human assessments than the scores from other predictors. The framework's modularity allows any features or predictors to be integrated into the facial attractiveness measure. Our proposed framework can be applied to many beauty-related fields, such as the plastic surgery, cosmetics, and entertainment industries.

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Section: Related Workmentioning

confidence: 99%

“…Fink et al [29] Skin Skin colors O'Toole et al [28] Shape, texture Shape, texture Liao et al [33] Facial ratio Golden ratio, symmetry, canons…”

Section: Dmentioning

confidence: 99%

A Novel Framework for Assessing Facial Attractiveness Based on Facial Proportions

et al. 2017

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“…In this experiment, we use the 66 facial feature points obtained by ISDM, while [1] requires a total of 84 feature points. We utilize global symmetrization and overall proportion optimization [17] on key facial features to calculate the beauty score of a face. A higher beauty score corresponds to a more attractive face in the training dataset.…”

Section: Facial Attractiveness Enhancementmentioning

confidence: 99%

Video Face Editing Using Temporal-Spatial-Smooth Warping

Liu

Deng

et al. 2016

ACM Trans. Intell. Syst. Technol.

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Editing faces in videos is a popular yet challenging aspect of computer vision and graphics, which encompasses several applications including facial attractiveness enhancement, makeup transfer, face replacement, and expression manipulation. Simply applying image-based warping algorithms to video-based face editing produces temporal incoherence in the synthesized videos because it is impossible to consistently localize facial features in two frames representing two different faces in two different videos (or even two consecutive frames representing the same face in one video). Therefore, high performance face editing usually requires significant manual manipulation. In this paper we propose a novel temporal-spatial-smooth warping (TSSW) algorithm to effectively exploit the temporal information in two consecutive frames, as well as the spatial smoothness within each frame. TSSW precisely estimates two control lattices in the horizontal and vertical directions respectively from the corresponding control lattices in the previous frame, by minimizing a novel energy function that unifies a data-driven term, a smoothness term, and feature point constraints. Corresponding warping surfaces then precisely map source frames to the target frames. Experimental testing on facial attractiveness enhancement, makeup transfer, face replacement, and expression manipulation demonstrates that the proposed approaches can effectively preserve spatial smoothness and temporal coherence in editing facial geometry, skin detail, identity, and expression, which outperform the existing face editing methods. In particular, TSSW is robust to subtly inaccurate localization of feature points and is a vast improvement over imagebased warping methods.

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“…Another system for the global enhancement of 3D scans is aimed not only at correcting asymmetry, but also to approach the proportions suggested by Neoclassic Canons, golden ratio and aesthetic criteria for the face profile ( [98]). Human raters preferred the enhanced 3D faces in 78.33% of the cases.…”

Section: D Scansmentioning

confidence: 99%

Computer analysis of face beauty: A survey

Laurentini

Bottino

2014

Computer Vision and Image Understanding

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The human face conveys to other human beings, and potentially to computer systems, information such as identity, intentions, emotional and health states, attractiveness, age, gender and ethnicity. In most cases analyzing this information involves the computer science as well as the human and medical sciences. The most studied multidisciplinary problems are analyzing emotions, estimating age and modeling aging effects. An emerging area is the analysis of human attractiveness. The purpose of this paper is to survey recent research on the computer analysis of human beauty. First we present results in human sciences and medicine pointing to a largely shared and datadriven perception of attractiveness, which is a rationale of computer beauty analysis. After discussing practical application areas, we survey current studies on the automatic analysis of facial attractiveness aimed at: i) relating attractiveness to particular facial features; ii) assessing attractiveness automatically; iii) improving the attractiveness of 2D or 3D face images. Finally we discuss open problems and possible lines of research.Index Terms-Face image analysis, face attractiveness, facial features, machine beauty analysis Introduction: Face Analysis ProblemsThe analysis of 2D or 3D images of humans is a main research topic in pattern analysis and computer vision.Applications exist, or are forecast, in a number of areas such as identity verification, natural man-machine interfaces, surveillance, forensics, sport performance enhancement, medical diagnosis and treatments.Special attention has been devoted to the images of hands ([175], [122]) and faces, the tools of most humanenvironment interaction. In particular, the human face is by far the part of the body which conveys most information to human beings, and thus potentially to computer systems ([108], [120]). Such information includes identity, intentions, health and emotional states, attractiveness, age, gender, ethnicity, attention and personality traits. In several cases the analysis of this information is a multidisciplinary problem, involving areas of computer science (such as pattern analysis and computer vision), human science (such as psychology, behavioral and cognitive sciences), and medicine.At present, the most studied and successful application of face image analysis is identity recognition or

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Enhancing the Symmetry and Proportion of 3D Face Geometry

Cited by 37 publications

References 24 publications

A Novel Framework for Assessing Facial Attractiveness Based on Facial Proportions

A Novel Framework for Assessing Facial Attractiveness Based on Facial Proportions

Video Face Editing Using Temporal-Spatial-Smooth Warping

Computer analysis of face beauty: A survey

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