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An Image Based Automatic 2D:4D Digit Ratio Measurement Procedure for Smart City Health and Business Applications

  • Frode Eika SandnesEmail author
  • Levent Neyse
Chapter

Abstract

2D:4D digit ratios are used for several health and business related applications. Currently, digit ratios are measured manually. This study proposes an automatic digit ratio measurement approach that can be used in the context of smart city healthcare and business applications. Smart city healthcare needs to be founded on the principles of self-service and independence. The proposed approach assumes that an image of the hands of a user is acquired using some imaging device. First, the hands are separated from the background. Next, the hand outline is traced. The hand outlines are used to identify points of interest that are used to measure the finger lengths and digit ratios. Experimental results are promising, but further research is needed before the approach can be deployed in real-world settings.

References

  1. 1.
    Putza DA, Gaulinb SJC, Sporterc RJ et al (2004) Sex hormones and finger length what does 2D:4D indicate? Evol Hum Behav 25:182–199CrossRefGoogle Scholar
  2. 2.
    Voracek M, Pietschnig J, Nader IW et al (2011) Digit ratio (2D:4D) and sex-role orientation: further evidence and meta-analysis. Personality Individ Differ 51:417–422CrossRefGoogle Scholar
  3. 3.
    McIntyre MH, Barrett ES, McDermott R et al (2007) Finger length ratio (2D:4D) and sex differences in aggression during a simulated war game. Personality Individ Differ 42:755–764CrossRefGoogle Scholar
  4. 4.
    Manning JT, Scutt D, Wilson J et al (1998) The ratio of 2nd to 4th digit length: a predictor of sperm numbers and concentrations of testosterone, luteinizing hormone and oestrogen. Hum Reprod 13:3000–3004CrossRefGoogle Scholar
  5. 5.
    Sandnes FE (2014) Measuring 2D: 4D finger length ratios with Smartphone Cameras. In: Proceedings of IEEE international conference on systems, man and cybernetics (SMC), IEEE, pp 1697–1701Google Scholar
  6. 6.
    Sandnes FE (2015) An automatic two-hand 2D:4D finger-ratio measurement algorithm for flatbed scanned images. In: Proceedings of IEEE international conference on systems, man and cybernetics (SMC), IEEE Computer Society Press, pp 1203–1208Google Scholar
  7. 7.
    Sandnes FE (2015) A Two-stage binarizing algorithm for automatic 2D:4D finger ratio measurement of hands with non-separated fingers. In: Proceedings of 11th international conference on innovations in information technology (IIT’15), IEEE, pp 178–183Google Scholar
  8. 8.
    Koch R, Haßlmeyer E, Tantinger D et al (2015) Development and implementation of algorithms for automatic and robust measurement of the 2D: 4D digit ratio using image data. Curr Dir Biomed Eng 1:220–223Google Scholar
  9. 9.
    Fukumoto M, Suenaga Y, Mase K (1994) Finger-pointer: pointing interface by image processing. Comput Graph 18:633–642CrossRefGoogle Scholar
  10. 10.
    Sauvola J, Pietikäinen M (2000) Adaptive document image binarization. Pattern Recogn 33:225–236CrossRefGoogle Scholar
  11. 11.
    Vezhnevets V, Sazonov V, Andreeva A (2003) A survey on pixel-based skin color detection techniques. Proc Graphicon 3:85–92Google Scholar
  12. 12.
    Kakumanu P, Makrogiannis S, Bourbakis N (2007) A survey of skin-color modeling and detection methods. Pattern Recogn 40:1106–1122CrossRefzbMATHGoogle Scholar
  13. 13.
    Pavlovic VI, Sharma R, Huang TS (1997) Visual interpretation of hand gestures for human-computer interaction: a review. IEEE Trans Pattern Anal 19:677–695CrossRefGoogle Scholar
  14. 14.
    Freeman WT, Roth M (1994) Orientation histograms for hand gesture recognition. Technical report. Mitsubishi Electric Research Laboratories, Cambridge Research Center, TR-94–03aGoogle Scholar
  15. 15.
    Neyse L, Brañas-Garza P (2014) Digit ratio measurement guide. No. 1914. Kiel Working PaperGoogle Scholar
  16. 16.
    Coetzee L, Botha EC (1993) Fingerprint recognition in low quality images. Pattern Recogn 26:1441–1460CrossRefGoogle Scholar
  17. 17.
    Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms (SODA’07). Society for Industrial and Applied Mathematics, Philadelphia, PA, USA, pp 1027–1035Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Oslo and Akershus University College of Applied SciencesOsloNorway
  2. 2.Westerdals Oslo School of Art, Communication and TechnologyOsloNorway
  3. 3.Kiel Institute for the World EconomyKielGermany

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