A New Face Beauty Prediction Model based on Blocked LBP

Guangming Lu, Xihua Xiao, Fangmei Chen

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.

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Paper Citation


in Harvard Style

Lu G., Xiao X. and Chen F. (2016). A New Face Beauty Prediction Model based on Blocked LBP . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 87-92. DOI: 10.5220/0005670500870092


in Bibtex Style

@conference{visapp16,
author={Guangming Lu and Xihua Xiao and Fangmei Chen},
title={A New Face Beauty Prediction Model based on Blocked LBP},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={87-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005670500870092},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - A New Face Beauty Prediction Model based on Blocked LBP
SN - 978-989-758-175-5
AU - Lu G.
AU - Xiao X.
AU - Chen F.
PY - 2016
SP - 87
EP - 92
DO - 10.5220/0005670500870092