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Age Estimation Using Sound Stimulation as a Hidden Biometrics Approach

  • Muhammad Ilyas
  • Alice Othmani
  • Amine Nait-aliEmail author
Chapter
Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

In this chapter, it will be introduced a new hidden biometrics approach of age estimation requiring the stimulation of the auditory system by an acoustical modulated sine wave signal. After a quick review on different common approaches used in the field of age estimation, and after presenting some generalities on the auditory system, age estimation and age classification protocols will be considered. This chapter describes also the concept of a simple identification/verification, as an application.

References

  1. 1.
    Moyse, E.: Age estimation from faces and voices: a review. Psychol. Belgica 54(3) (2014)Google Scholar
  2. 2.
    Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. IEEE Trans. Inf. Forensics Secur. 9(12), 2170–2179 (2014)CrossRefGoogle Scholar
  3. 3.
    Lanitis, A., Draganova, C., Christodoulou, C.: Comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(1), 621–628 (2004)CrossRefGoogle Scholar
  4. 4.
    Freire-Aradas, A., Phillips, C., Lareu, M.V.: Forensic individual age estimation with DNA: from initial approaches to methylation tests. Forensic Sci. Rev. 29(2) (2017)Google Scholar
  5. 5.
    Williams, G.: A review of the most commonly used dental age estimation techniques. J. Forensic Odontostomatol. 19(1), 9–17 (2001)MathSciNetGoogle Scholar
  6. 6.
    Shafran, I., Riley, M., Mohri, M.: Voice signatures. In: 2003 IEEE Workshop on Automatic Speech Recognition and Understanding, 2003. ASRU’03, pp. 31–36. IEEE (2003)Google Scholar
  7. 7.
    Metze, F., Ajmera, J., Englert, R., Bub, U., Burkhardt, F., Stegmann, J., Muller, C., Huber, R., Andrassy, B., Bauer, J.G., Littel, B.: Comparison of four approaches to age and gender recognition for telephone applications. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 2007. ICASSP 2007, vol. 4, pp. IV-108. IEEE (2007)Google Scholar
  8. 8.
    Dobry, G., Hecht, R.M., Avigal, M., Zigel, Y.: Supervector dimension reduction for efficient speaker age estimation based on the acoustic speech signal. IEEE Trans. Audio Speech Lang. Process. 19(7), 1975–1985 (2011)CrossRefGoogle Scholar
  9. 9.
    Lu, J., Tan, Y.P.: Gait-based human age estimation. IEEE Trans. Inf. Forensics Secur. 5(4), 761–770 (2010)CrossRefGoogle Scholar
  10. 10.
    Makihara, Y., Okumura, M., Iwama, H., Yagi, Y.: Gait-based age estimation using a whole-generation gait database. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1–6. IEEE (2011)Google Scholar
  11. 11.
    Tsimperidis, G., Katos, V., Rostami, S.: Age detection through keystroke dynamics from user authentication failures. Int. J. Dig. Crime Forensics (IJDCF) 9(1), 1–16 (2017)CrossRefGoogle Scholar
  12. 12.
    Uzun, Y., Bicakci, K., Uzunay, Y.: Could We Distinguish Child Users from Adults Using Keystroke Dynamics? (2015). arXiv preprint arXiv:1511.05672
  13. 13.
    Smith, S.W.: The Scientist and Engineer’s Guide to Digital Signal Processing, p. 35 (1997)Google Scholar
  14. 14.
    Zwicker, E.: Subdivision of the audible frequency range into critical bands (frequenzgruppen). J. Acoust. Soc. Am. 33, 248 (1961)CrossRefGoogle Scholar
  15. 15.
    Stuart, R., Howell, P.: Signals and Systems for Speech and Hearing. 2nd edn., pp. 163. BRILL (2011)Google Scholar
  16. 16.
    Rossing, T.: Springer Handbook of Acoustics, 1st edn., pp. 747–748. Springer (2007)Google Scholar
  17. 17.
    Ilyas, M., Othmani, A., Nait-Ali, A.: Human age estimation using auditory system through dynamic frequency sound. In: IEEE 2nd International Conference on Bio-engineering for Smart Technologies (BioSMART) (2017)Google Scholar
  18. 18.
    Stockwell, C.W., Ades, H.W., Engström, H.: XCVII patterns of hair cell damage after intense auditory stimulation. Ann. Otol. Rhinol. Laryngol. Suppl. 78, 1144–1168 (2017)CrossRefGoogle Scholar
  19. 19.
    Manley, G.A., van Dijk, P.: Frequency selectivity of the human cochlea: suppression tuning of spontaneous otoacoustic emissions. Hear Res. 336, 53–62 (2016)CrossRefGoogle Scholar
  20. 20.
    Paolis, A.D., Bikson, M., Nelson, J.T., de Ru, J.A., Packe, M., Cardoso, L.: Analytical and numerical modeling of the hearing system: Advances towards the assessment of hearing damage. Hear. Res. 349, 111–128 (2017)CrossRefGoogle Scholar
  21. 21.
    Barbosa de Sá, L.C., Lima, M.A.M.T., Tomita, S., Frota, S.M.M.C., Santos, G.A., Garcia, T.R.: Analysis of high frequency auditory thresholds in individuals aged between 18 and 29 years with no ontological complaints. Rev. Bras. Otorrinolaringol. 73, 2 (2007)Google Scholar
  22. 22.
    Breiman, L.: Random forests. Mach. Learn. 45, 123–140 (2011)Google Scholar
  23. 23.
    Guyon, I., Saffari, A., Dror, G., Cawley, G.: Model selection: beyond the bayesian–frequentist divide. JMLR 11, 61–87 (2010)MathSciNetzbMATHGoogle Scholar
  24. 24.
    Anguita, D., Ghio, A., Ridella, S., Sterpi, D.: K-fold cross validation for error rate estimate in support vector machines. In: Proceedings of the International Conference on Data Mining, USA, pp. 291–297 (2009)Google Scholar
  25. 25.
    Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)CrossRefGoogle Scholar
  26. 26.
    Statnikov, A., Tsamardinos, I., Dosbayev, Y., Aliferis, C.F.: GEMS: a system for automated cancer diagnosis and biomarker discovery from microarray gene expression data. Int. J. Med. Inform 74, 491–503 (2005)CrossRefGoogle Scholar
  27. 27.
    Scheffer, T.: Error estimation and model selection. Ph.D. Thesis, Technischen Universität Berlin, School of Computer Science (1999)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Université Paris-Est, LISSI, UPECVitry sur SeineFrance

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