A New Face Beauty Prediction Model based on Blocked LBP

Guangming Lu, Xihua Xiao, Fangmei Chen

2016

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.

References

  1. Eisenthal Y., Dror G., Ruppin E., 2006. Face Attractiveness: beauty and the machine, Neural Computation, pp. 110-150.
  2. Kagian A., Dror G., LeyVand T., Cohen-Or D., Ruppin E., 2008. A humanlike predictor of face attractiveness, Advance Neural Information Processing Systems, pp. 676-683.
  3. Aarabi P., Hughes D., Mohajer K., et al., 2001. The automatic measurement of face beauty, Proc of the IEEE International Conference on System Man and Cybernetics, pp. 2644-2647.
  4. Irem H., Turkmen Z., Kurt M., et al., 2007. Global feature based female face beauty decision system, Proceedings of the 15th European Signal Processing Conference, Lausanne, Switzerland, pp. 1945-1949.
  5. Gunes H., Piccardi M., 2006. Assessing face beauty through proportion analysis by image processing and supervised learning, International Journal of Human-Computer Studies, pp. 1184-1199.
  6. Schmid K., Marx D., and Samal A., 2008. Computation of a face attractive- ness index based on neoclassical canons, symmetry, and golden ratios, Pattern Recognition, pp. 2710-2717.
  7. Douglas G., Kai Y., Wei X., Gong Y., 2010. Predicting face beauty without landmarks, Computer Vision-ECCV 2010 Lecture Notes in Computer Science, pp. 434-447.
  8. Zhang D., Zhao Q., Chen F., 2011. Quantitative analysis of human face beauty using geometric features, Pattern Recognition, pp. 940-950.
  9. Mao H., Chen Y., Jin L. and Du M., 2011. Evaluating face attractiveness: a gabor feature approach, Journal of Communication and Computer, pp. 674-679.
  10. Gan J., Li L., Zhai Y., et al., 2014. Deep self-taught learning for facial beauty prediction. Neurocomputing, 2014, pp. 295-303.
  11. Cootes T., Taylor C., et al., 1995. Active shape models---their training and application, Computer Vision and Image Understanding, pp. 38-59.
  12. Sukno F., Ordas S., Butakoff C., Cruz S., Frangi A., 2007. Active shape models with invariant optimal features: application to facial analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1105-1117.
  13. Cui Y., Jin Z., Yang W., 2012. A two steps face alignment approach using statistical models, International Journal of Advanced Robotic System, pp. 1-6.
  14. Guo Z., Zhang L., Zhang D., 2010. Rotation invariant texture classification using LBP variance (LBPV) with global matching, Pattern Recognition, pp. 706-719.
  15. Jia M. Zhang Z., Song P., Du J., 2014. Research of Improved Algorithm Based on LBP for Face Recognition. Biometric Recognition Lecture Notes in Computer Science, pp. 111-119.
  16. Pearson K., 1920. Notes on the history of correlation royal society. Proceedings Biometrika, pp. 25-45.
  17. Rodgers J., Nicewander W., 1988. Thirteen ways to look at the correlation coefficient, The American Statistician, pp. 59-66.
Download


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