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.
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References
Moyse, E.: Age estimation from faces and voices: a review. Psychol. Belgica 54(3) (2014)
Eidinger, E., Enbar, R., Hassner, T.: Age and gender estimation of unfiltered faces. IEEE Trans. Inf. Forensics Secur. 9(12), 2170–2179 (2014)
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)
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)
Williams, G.: A review of the most commonly used dental age estimation techniques. J. Forensic Odontostomatol. 19(1), 9–17 (2001)
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)
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)
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)
Lu, J., Tan, Y.P.: Gait-based human age estimation. IEEE Trans. Inf. Forensics Secur. 5(4), 761–770 (2010)
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)
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)
Uzun, Y., Bicakci, K., Uzunay, Y.: Could We Distinguish Child Users from Adults Using Keystroke Dynamics? (2015). arXiv preprint arXiv:1511.05672
Smith, S.W.: The Scientist and Engineer’s Guide to Digital Signal Processing, p. 35 (1997)
Zwicker, E.: Subdivision of the audible frequency range into critical bands (frequenzgruppen). J. Acoust. Soc. Am. 33, 248 (1961)
Stuart, R., Howell, P.: Signals and Systems for Speech and Hearing. 2nd edn., pp. 163. BRILL (2011)
Rossing, T.: Springer Handbook of Acoustics, 1st edn., pp. 747–748. Springer (2007)
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)
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)
Manley, G.A., van Dijk, P.: Frequency selectivity of the human cochlea: suppression tuning of spontaneous otoacoustic emissions. Hear Res. 336, 53–62 (2016)
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)
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)
Breiman, L.: Random forests. Mach. Learn. 45, 123–140 (2011)
Guyon, I., Saffari, A., Dror, G., Cawley, G.: Model selection: beyond the bayesian–frequentist divide. JMLR 11, 61–87 (2010)
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)
Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)
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)
Scheffer, T.: Error estimation and model selection. Ph.D. Thesis, Technischen Universität Berlin, School of Computer Science (1999)
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Ilyas, M., Othmani, A., Nait-ali, A. (2020). Age Estimation Using Sound Stimulation as a Hidden Biometrics Approach. In: Nait-ali, A. (eds) Hidden Biometrics. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-0956-4_7
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