GAN Junying, ZHAI Yikui, WANG Bin. Unconstrained Facial Beauty Prediction Based on Multi-scale K-Means[J]. Chinese Journal of Electronics, 2017, 26(3): 548-556. doi: 10.1049/cje.2016.10.020
Citation: GAN Junying, ZHAI Yikui, WANG Bin. Unconstrained Facial Beauty Prediction Based on Multi-scale K-Means[J]. Chinese Journal of Electronics, 2017, 26(3): 548-556. doi: 10.1049/cje.2016.10.020

Unconstrained Facial Beauty Prediction Based on Multi-scale K-Means

doi: 10.1049/cje.2016.10.020
Funds:  This work is supported by the National Natural Science Fund of China (No.61072127, No.61372193, No.61070167), NSF of Guangdong Province, P.R.C. (No.S2013010013311, No.10152902001000002, No.S2011010001085, No.S2011040004211), Higher Education Outstanding Young Teachers Foundation of Guangdong Province (No.SYQ2014001), and Youth Foundation of Wuyi University (No.2013zk07).
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  • Corresponding author: ZHAI Yikui (corresponding author) received the B.S. and M.S. degrees in optical electronics information and communication engineering, and electrical information engineering from Shantou University in 2004 and 2007, respectively. He received the Ph.D. degree in Beijing University of Aeronautics and Astronautics in 2013, and was approved associate professor in Wuyi University since 2013. His research interests include image processing and pattern recognition.
  • Received Date: 2015-07-12
  • Rev Recd Date: 2015-09-09
  • Publish Date: 2017-05-10
  • Facial beauty prediction belongs to an emerging field of human perception nature and rule. Compared with other facial analysis tasks, this task has shown its challenges in pattern recognition and biometric recognition. The algorithm of presented facial beauty prediction requires burden landmark or expensive optimization procedure. We establish a larger database and present a novel method for predicting facial beauty, which is notably superior to previous work in the following aspects:1) A largescale database with more reasonable distribution has been established and utilized in our experiments; 2) Both female and male facial beauties are analyzed under unconstrained conditions without landmark; 3) Multi-scale apparent features are learned to represent facial beauty which are more expressive and require less computation expenditure. Experimental results demonstrate the accuracy and efficiency of the presented method.
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