Authors:
Guangming Lu
1
;
Xihua Xiao
1
and
Fangmei Chen
2
Affiliations:
1
Harbin Institute of Technology, China
;
2
Tsinghua University, China
Keyword(s):
Face Beauty, ASMs, Texture Feature, Blocked-LBP.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Entertainment Imaging Applications
;
Features Extraction
;
Image and Video Analysis
Abstract:
In recent years, many scholars use machine learning methods to analyze facial beauty and achieve some good results, but there are still some problems needed to be considered, for instance, the face beauty degrees are not widely distributed, and previous works emphasized more on face geometry features, rather than texture features. This paper proposes a novel face beauty prediction model based on Blocked Local Binary Patterns (BLBP). First, we obtain the face area by ASMs model, then, the BLBP algorithm is proposed in accordance with texture features. Finally, we use Pearson correlation coefficient between the output of the facial beauty by our algorithm and subjective judgments by the raters for evaluation. Experimental results show that the method can predict the beauty of face images automatically and effectively.