Skip to main content

Beauty Analysis Fusion Model of Texture and Geometric Features

  • Chapter
  • First Online:
Computer Models for Facial Beauty Analysis

Abstract

Most of previous studies use only one kind of features for facial beauty analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aarabi P, Hughes D, Mohajer K, Emami M (2001) The automatic measurement of face beauty. In: Proceeding of IEEE international conference on system man and cybernetics, pp 2644–2647

    Google Scholar 

  • Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27. http://www.csie.ntu.edu.tw/_cjlin/libsvm

    Google Scholar 

  • Chen F, Zhang D (2010) A benchmark for geometric face beauty study. Med Biometr 6165:21–32

    Article  Google Scholar 

  • Chen Y, Zhou X, Huang TS (2001) One-Class SVM for learning in image retrieval. In: Proceeding of international conference on image processing, vol 1, pp 34–37

    Google Scholar 

  • Gray D, Yu K, Xu W, Gong Y (2010) Predicting face beauty without landmarks. In: Proceedings of European conference on computer vision, pp 434–447

    Google Scholar 

  • Gunes H, Piccardi M (2006) Assessing face beauty through proportion analysis by image processing and. Int J Hum Comput Stud 64(12):1184–1199

    Article  Google Scholar 

  • Guo Z, Zhang L, Zhang D (2010) Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recogn 43(3):706–719

    Article  MATH  Google Scholar 

  • Huang X, Li SZ, Wang Y (2004) Shape localization based on statistical method using extended local binary pattern. In: Proceeding of IEEE first symposium on multi-agent security and survivability, pp 18–20

    Google Scholar 

  • Irem H, Turkmen Z, Kurt M, Karsligil M E (2007) Global feature based face beauty decision system. In: Proceedings of the 15th European signal processing conference, pp 1945–1949

    Google Scholar 

  • Liu CL, Lee CH, Lin PM (2010) A fall detection system using. Expert Syst Appl 37(10):7174–7181

    Article  Google Scholar 

  • Mao H, Chen Y, Jin L, Du M (2011) Evaluating face attractiveness: an gabor feature approach. J Commun Comput 8674–679

    Google Scholar 

  • Pearson K (1920) Notes on the history of correlation. Biometrika 13(1):25–45

    Article  Google Scholar 

  • Rodgers JL, Nicewander WA (1988) Thirteen ways to look at the correlation. Am Stat 42(1):59–66

    Article  Google Scholar 

  • Xie K, Song Q, Zhou J (2002) A linear regress model based on least absolute criteria. J Syst Simul 14(2):189–192

    Google Scholar 

  • Zhang D, Zhao Q, Chen F (2011) Quantitative analysis of human face beauty using. Pattern Recogn 44(4):940–950

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David Zhang .

Appendix 1

Appendix 1

We defined the starting 40 geometric feature s in Table 6.3, where the ‘X’ denotes the horizontal direction and ‘Y’ denotes the vertical direction. Let \( A(x_{1} ,\,y_{1} ) \) and \( B(x_{2} ,\,y_{2} ) \) be the landmark points, then ‘XA–XB’ denotes the distance of the horizontal direction and is equal to \( (x_{1} - x_{2} ) \), ‘YA–YB’ denotes the distance of the vertical direction and is equal to \( (y_{1} - y_{2} ) \). The 77 landmark points are shown in Fig. 6.1b.

Table 6.3 The starting 40 geometric feature s

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Zhang, D., Chen, F., Xu, Y. (2016). Beauty Analysis Fusion Model of Texture and Geometric Features. In: Computer Models for Facial Beauty Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-32598-9_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32598-9_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32596-5

  • Online ISBN: 978-3-319-32598-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics