地标
计算机科学
判别式
吸引力
人工智能
面子(社会学概念)
模式识别(心理学)
特征(语言学)
可预测性
计算
普鲁克分析
支持向量机
统计的
计算机视觉
数学
统计
算法
哲学
社会学
精神分析
语言学
社会科学
心理学
作者
Shu Liu,Yangyu Fan,Zhe Guo,Ashok Samal,Afan Ali
标识
DOI:10.1016/j.neucom.2017.01.050
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 (R2=0.5756).
科研通智能强力驱动
Strongly Powered by AbleSci AI