Abstract
This paper presents statistical analyses for facial beauty study. A large-scale database was built, containing 23412 frontal face images, 875 of them are marked as beautiful. We focus on the geometric feature defined by a set of landmarks on faces. A normalization approach is proposed to filter out the non-shape variations – translation, rotation, and scale. The normalized features are then mapped to its tangent space, in which we conduct statistical analyses: Hotelling’s T 2 test is applied for testing whether female and male mean faces have significant difference; Principal Component Analysis (PCA) is applied to summarize the main modes of shape variation and do dimension reduction; A criterion based on the Kullback-Leibler (KL) divergence is proposed to evaluate different hypotheses and models. The KL divergence measures the distribution difference between the beautiful group and the whole population. The results show that male and female faces come from different Gaussian distributions, but the two distributions overlap each other severely. By measuring the KL divergence, it shows that multivariate Gaussian model embodies much more beauty related information than the averageness hypothesis and the symmetry hypothesis. We hope the large-scale database and the proposed evaluation methods can serve as a benchmark for further studies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Etcoff, N.L.: Beauty and the beholder. Nature 368, 186–187 (1994)
Langlois, J.H., Roggman, L.A.: Attractive Faces are only Average. Psychological Science 1, 115–121 (1990)
Perrett, D.I., Burt, D.M., Penton-Voak, I.S., Lee, K.J., Rowland, D.A., Edwards, R.: Symmetry and Human Facial Attractiveness. Evolution and Human Behavior 20, 295–307 (1999)
Perrett, D.I., May, K.A., Yoshikawa, S.: Facial Shape and Judgements of Female Attractiveness. Nature 368, 239–242 (1994)
Wang, D., Qian, G., Zhang, M., Leslie, G.F.: Differences in Horizontal, Neoclassical Facial Cannons in Chinese (Han) and North American Caucasian Populations. Aesthetic Plastic Surgery 21, 265–269 (1997)
Pallett, P.M., Link, S., Lee, K.: New ‘golden’ ratios for facial beauty. Vision Research 50(2), 149–154 (2010)
Schmid, K., Marx, D., Samal, A.: Computation of a face attractiveness index based on neoclassical canons, symmetry and golden ratios. Pattern Recognition (2007)
Rhodes, G., Yoshikawa, S., Clark, A., Lee, K., McKay, R., Akamatsu, S.: Attractiveness of Facial Averageness and Symmetry in non-Western Cultures: In Search of Biologically based Standards of Beauty. Perception 30, 611–625 (2001)
Valenzano, D.R., Mennucci, A., Tartarelli, G., Cellerino, A.: Shape Analysis of female facial attractiveness. Vision Research 46, 1282–1291 (2006)
Leyvand, T., Cohen-Or, D., Dror, G., Lischinski, D.: Data-Driven Enhancement of Facial Attractiveness. In: ACM SIGGRAPH (2008)
Davis, B.C., Lazebnik, S.: Analysis of Human Attractiveness Using Manifold Kernel Regression. In: ICIP (2008)
Liu, J., Yang, X.-b., Xi, T.-t., Gu, L.-x., Yu, Z.-y.: A Novel Method for Computer Aided Plastic Surgery Prediction (2009)
Whitehill, J., Movellan, J.R.: Personalized Facial Attractive Prediction (2008)
Eisenthal, Y., Dror, G., Ruppin, E.: Facial Attractiveness: Beauty and the Machine. Neural Computation 18, 119–142 (2006)
Atiyeh, B.S., Hayek, S.N.: Numeric Expression of Aesthetics and Beauty. Aesthetic Plastic Surgery 32, 209–216 (2008)
Adams, D.C., James Rohlf, F., Slice, D.E.: Geometric morphometrics: ten years of progress following the ‘revolution’. Ital. J. Zool. 71, 5–16 (2004)
Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57(2), 137–154 (2004)
Rowley, H.A., Baluja, S., Kanade, T.: Neural Network-Based Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence 20, 23–38 (1998)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active Shape Models - Their Training and Application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Sukno, F.M., Ordas, S., Butakoff, C., Cruz, S., Frangi, A.F.: Active Shape Models with Invariant Optimal Features: Application to Facial Analysis. IEEE Trans. Pattern Analysis and Machine Intelligence 29(7), 1105–1117 (2007)
Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. John Wiley, Chichester (1998)
Liu, C., Wechsler, H.: Robust Coding Schemes for Indexing and Retrival from Large Face Databases. IEEE Trans. Image Processing 9, 132–137 (2000)
Thode, H.C.: Tesing for normality. Marcel Dekker, New York (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, F., Zhang, D. (2010). A Benchmark for Geometric Facial Beauty Study. In: Zhang, D., Sonka, M. (eds) Medical Biometrics. ICMB 2010. Lecture Notes in Computer Science, vol 6165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13923-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-13923-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13922-2
Online ISBN: 978-3-642-13923-9
eBook Packages: Computer ScienceComputer Science (R0)