Skip to main content
SpringerLink
Log in

Modelling of color perception of different eye colors using artificial neural networks

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Color is a subjective term that changes according to each person’s view. We should define color physically to work with it in terms of printing or any other professional study. However, enabling the observation of color in terms of sales, marketing, product design, and development is as important as product and its packaging. The perception of color and tendencies to colors will be different for people of different eye colors. In this study, different color samples will be shown to five different eye colors of hazel, green, blue, brown, and black eyes both of men and women. The aim of the study is to determine which eye color is more perceived and adopted by which eye color using artificial neural network (ANN) for between 6 and 17 ages. By considering these determinations, it has been studied with graphical and statistical illustrations how different eye color groups prefer colors, how much they are able to recognize primary and secondary colors, and to what extent various eye colors are able to perceive RGB and CMY colors correctly.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Battiti R (1992) First and second order methods for learning: between steepest descent and Newton’s method. Neural Comput 4(2):141–166

    Article  Google Scholar 

  2. Bechtler H, Browne MW, Bansal PK, Kecman V (2001) New approach to dynamic modeling of vapour-compression liquid chillers: artificial neural networks. Appl Therm Eng 21(9):941–953

    Article  Google Scholar 

  3. Chouai A, Laugier S, Richon D (2002) Modeling of thermodynamic properties using neural networks: application to refrigerants. Fluid Phase Equilib 199(1–2):53–62

    Article  Google Scholar 

  4. Color Tune 3.0 Macintosh User’s Guide (1997) Agfa-Gevaert

  5. Color ICC Output Profiles for Production Process and Proofing Devices (1999) IFRA

  6. Color and Color Management (1999) X-rite, Technical bulletin, The Secrets of Color Management. Agfa-Gevaert, Belgium

  7. Hagan M, Menhaj M (1994) Training feed-forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993

    Article  Google Scholar 

  8. Kalogirou SA (2001) Applications of artificial neural-networks for energy systems. Appl Energy 67(1–2):17–35

    Google Scholar 

  9. Karataş C, Sozen A, Arcaklıog˘lu E, Ergüney S (2007) Modelling of yield length in the mould of commercial plastics using artificial neural networks. Mater Des 28(1):278–286

    Article  Google Scholar 

  10. Kose E, Selimbeyoğlu C (2006) Color perception of men in different age groups. Karaelmas University, Karabük Technical Education Faculty. J Sci 9(3):171–179

    Google Scholar 

  11. Köse E, Şahinbaşkan T, Güler İ (2009) The investigation of effects of digital proofing systems used in color management on print quality with neural networks. Expert Syst Appl 36(1):745–754

    Article  Google Scholar 

  12. Marquardt D (1963) An algorithm for least squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441

    Article  MathSciNet  MATH  Google Scholar 

  13. Mellit A, Benghanemb M, Kalogirou SA (2007) Modeling and simulation of a standalone photovoltaic system using an adaptive artificial neural network: Proposition for a new sizing procedure. J Int Renew Energy 32(2):285–313

    Article  Google Scholar 

  14. Mohandes M, Rehman S, Halawani TO (1998) Estimation of global solar radiation using artificial neural networks. J Int Renew Energy 14(1–4):179–184

    Article  Google Scholar 

  15. Palau A, Velo E, Puigjaner L (1999) Use of neural networks and expert systems to control a gas/solid sorption chilling machine. Int J Refrig 22(1):59–66

    Article  Google Scholar 

  16. Reddy KS, Ranjan M (2003) Solar resource estimation using artificial neural networks and comparison with other correlation models. Energy Convers Manage 37(2):183–198

    Google Scholar 

  17. Selimbeyoğlu C, Ural ÖU, Sönmez S, Kurt B (2003) The perception of color to different age groups. In: 1st international printing technologies symposium, pp 412–424

  18. Sozen A, Arcaklioglu E, Ozalp M (2004) Investigation of thermodynamic properties of refrigerant/absorbent couples using artificial neural networks. Chem Eng Process 43:1253–1264

    Article  Google Scholar 

  19. Sozen A, Arcaklıog˘lu E, Özalp M, Kanıt EG (2004) Use of artificial neural networks for mapping the solar potential in Turkey. Appl Energy 77(3):273–286

    Article  Google Scholar 

  20. Scanning for Color Management (2001) Hutcheson consulting, technical bulletin

  21. Şahinbaşkan T, Selimbeyoğlu C (2000) Reproduction technologies class notes. Marmara University Faculty of Technical Education, Istanbul

    Google Scholar 

  22. www.biochem.uni-erlangen.de/forschung/arbeits

  23. www.brps.org.uk/index.php?pageid=202&tln=aboutrp

Download references

Author information

Authors and Affiliations

  1. Printing Education Department, Technical Education Faculty, Marmara University, Istanbul, Turkey

    Candan Cengiz

  2. Printing Education Department, Technical Education Faculty, Gazi University, Ankara, Turkey

    Erdoğan Köse

Authors
  1. Candan Cengiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erdoğan Köse
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Candan Cengiz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cengiz, C., Köse, E. Modelling of color perception of different eye colors using artificial neural networks. Neural Comput & Applic 23, 2323–2332 (2013). https://doi.org/10.1007/s00521-012-1185-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-1185-x

Keywords

Search

Navigation