A landmark-based data-driven approach on 2.5D facial attractiveness computation

被引：20

作者：

Liu, Shu ^{[1
,2
]}

Fan, Yang-Yu ^{[1
]}

Guo, Zhe ^{[1
]}

Samal, Ashok ^{[2
]}

Ali, Afan ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China

[2] Univ Nebraska, Dept Comp Sci & Engn, Lincoln, NE 68588 USA

来源：

NEUROCOMPUTING | 2017年 / 238卷

基金：

中国国家自然科学基金;

关键词：

Facial attractiveness computation; 2.5; D; Geometric features; Data-driven; BJUT-3D; BEAUTY; CLASSIFICATION; PREDICTION;

D O I：

10.1016/j.neucom.2017.01.050

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Investigating the nature and components of face attractiveness from a computational view has become an emerging topic in facial analysis research. In this paper, a multi-view (frontal and profile view, 2.5D) facial attractiveness computational model is developed to explore how face geometry affects its attractiveness. A landmark-based, data-driven method is introduced to construct a huge dimension of three kinds of geometric facial measurements, including ratios, angles, and inclinations. An incremental feature selection algorithm is proposed to systematically select the most discriminative subset of geometric features, which are finally mapped to an attractiveness score through the application of support vector regression (SVR). On a dataset of 360 facial images pre-processed from BJUT-3D Face Database and an attractiveness score dataset collected from human raters, we show that the computational model performs well with low statistic error (MSE = 0.4969) and good predictability (R-2 = 0.5756). (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：168 / 178

页数：11

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