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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  • D. Zhang, Q. Zhao and F. Chen, "Quantitative analysis of human facial beauty using geometric features", Pattern Recognition, Vol.44, No.4, pp.940-950, 2011.
    A. Kagian, G. Dror, T. Leyvand, et al., "A machine learning predictor of facial attractiveness revealing human-like psychophysical biases", Vision Research, Vol.48, No.2, pp.235-243, 2008.
    C.D. Green, "All that glitters:A review of psychological research on the aesthetics of the golden section", PerceptionLondon, Vol.24, pp.937-937, 1995.
    P.M. Pallett, S. Link, et al., "New "golden" ratios for facial beauty", Vision Research, Vol.50, No.2, pp.149-154, 2010.
    H.Ï. Türkmen, Z. Kurt, et al., "Global feature based female facial beauty decision system", 15th European Signal Processing Conference (EUSIPCO 2007), Poznan, Poland, pp.1945-1949, 2007.
    J. Gan, L. Li, Y. Zhai, et al., "Deep self-taught learning for facial beauty prediction", Neurocomputing, Vol.144, pp.295-303, 2014.
    D. Gray, K. Yu, W. Xu, et al., "Predicting facial beauty without landmarks", European Conference on Computer Vision (ECCV 2010), Springer Berlin Heidelberg, pp.434-447, 2010.
    Y. Eisenthal, G. Dror and E. Ruppin, "Facial beauty:Beauty and the machine", Neural Computation, Vol.18, No.1, pp.119-142, 2006.
    J. Wang, Y. Gong and D. Gray, "Female facial beauty attribute recognition and editing", Human-Centered Social Media Analytics, Springer International Publishing, pp.133-148, 2014.
    A. Coates, A.Y. Ng and H. Lee, "An analysis of single-layer networks in unsupervised feature learning", International Conference on Artificial Intelligence and Statistics, pp.215-223, 2011.
    M. Weber, et al., "Unsupervised learning of models for recognition", Eccv 2000, LNCS, Springer Berlin Heidelberg, Vol.1842, pp.18-32, 2000.
    Y. Mu, "Computational facial attractiveness prediction by aesthetics-aware features", Neurocomputing, Vol.99, pp.59-64, 2013.
    H. Yan, "Cost-sensitive ordinal regression for fully automatic facial beauty assessment", Neurocomputing, Vol.129, pp.334-342, 2014.
    A. Laurentini and A. Bottino, "Computer analysis of face beauty:A survey", Computer Vision and Image Understanding, Vol.125, pp.184-199, 2014.
    K. Schmid, D. Marx and A. Samal, "Computation of a face beauty index based on neoclassical canons, symmetry, and golden ratios", Pattern Recognition, Vol.41, No.8, pp.2710-2717, 2008.
    H. Gunes and M. Piccardi, "Assessing facial beauty through proportion analysis by image processing and supervised learning", International Journal of Human-Computer Studies, Vol.64, No.12, pp.1184-1199, 2006.
    P. Viola and M.J. Jones, "Robust real-time face detection", International Journal of Computer Vision, Vol.57, No.2, pp.137-154, 2004.
    L.G. Farkas and I.R. Munro, Anthropometric Facial Proportions in Medicine, Charles C. Thomas Publisher, Springfield Iuinois USA, 1987.
    J. Bekios-Calfa, J.M. Buenaposada and L. Baumela, "Revisiting linear discriminant techniques in gender recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.33, No.4, pp.858-864, 2011.
    K. Kościński, "Current status and future directions of research on facial attractiveness", Anthropological Review, Vol.72, No.1, pp.45-65, 2009.
    M.R. Cunningham, A.R. Roberts, A.P. Barbee, et al., "‘Their ideas of beauty are, on the whole, the same as ours’:Consistency and variability in the cross-cultural perception of female physical attractiveness", Journal of Personality and Social Psychology, Vol.68, No.2, pp.261, 1995.
    D.I. Perrett, K.J. Lee, I. Penton-Voak, et al., "Effects of sexual dimorphism on facial attractiveness", Nature, Vol.394, No.6696, pp.884-887, 1998.
    H. Knight and O. Keith, "Ranking facial attractiveness", The European Journal of Orthodontics, Vol.27, No.4, pp.340-348, 2005.
    J.C. Van Gemert, J.M. Geusebroek, C.J. Veenman, et al., "Kernel codebooks for scene categorization", Computer Vision-ECCV 2008, Springer Berlin Heidelberg, pp.696-709, 2008.
    A. Coates and A.Y. Ng, "Learning feature representations with k-means", Neural Networks:Tricks of the Trade, Springer Berlin Heidelberg, pp.561-580, 2012.
    H. Lee, R. Grosse, R. Ranganath, et al., "Unsupervised learning of hierarchical representations with convolutional deep belief networks", Communications of the ACM, Vol.54, No.10, pp.95-103, 2011.
    M.D. Zeiler, D. Krishnan, G.W. Taylor, et al., "Deconvolutional networks", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2528-2535, 2010.
    J. Liu, J.P. Du and X.R. Wang, "Research on the robust image representation scheme for natural scene categorization", Chinese Journal of Electronics, Vol.22, No.2, pp.341-346, 2013.
    T.M. Mitchell, Machine Learning, McGraw Hill, Burr Ridge, IL, USA 1997.
    Powers and M.W. David, "Evaluation:From precision, recall and F-factor to ROC, informedness, markedness & correlation", Journal of Machine Learning Technologies, Vol.2, No.1, pp.37-63, 2007.
    C.-C. Chang and C.-J. Lin, "LIBSVM:A library for support vector machines", ACM Transactions on Intelligent Systems and Technology (TIST), Vol.2, No.3, Article No.27, 2011.
    R.L. Lang, X.L. Deng and F. Gao, "The heuristic algorithms for selecting the parameters of support vector machine for classification", Chinese Journal of Electronics, Vol.21, No.3, pp.485-488, 2012.
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