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Biometric recognition system based on the motion of the human body gravity centre analysis

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper we present a novel approach that enables the determination and measurement of important features associated with the human body movement. This information can be used in the construction of a biometric personal identification system. Biometrics is, essentially, a pattern recognition system based on measurements of unique physiological or behavioural features as acquired from an individual. The domain of biometric techniques is currently placed within recently developed disciplines of science. Biometry or biometrics is simply defined as automatically recognizing a person using distinguishing traits and is widely used in various security systems. Biometry can be defined as a method of personal identification based on individuals' physical and behavioural features. Physiological biometrics covers data coming directly from a measurement of part of a human body, for example a fingerprint, the shape of the face, or from the retina. Behavioural biometrics analyses data obtained on the basis of an activity performed by a given person, for example speech and the handwritten signature. The system of biometrics defined above can now be expanded, and a new biometrics system can be considered. In our approach, human foot pressure on a surface is measured and the pressure data retrieved. The pressure parameters are collected without the necessity of any movements of the feet.
Rocznik
Tom
Strony
61--69
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
  • University of Silesia, Institute of Computer Science, 41-200 Sosnowiec, Będzińska 39, Poland
autor
autor
autor
Bibliografia
  • [1] CHANDZLIK S., PIECHA J.: A patient walk–data–record modeling using a spline interpolation method. Journal of Medical Informatics & Technologies. Vol. 3, 2002, pp. 153–160.
  • [2] CHANDZLIK S., PIECHA J.: The body balance measures for neurological disease estimation and classification. Journal of Medical Informatics & Technologies, Vol. 6, 2003, pp. 87–94.
  • [3] GIORGINO T., Computing and Visualizing Dynamic Time Warping Alignments in R: The DTE Package. Journal of Statistical Software, 2009.
  • [4] HONG Y, KOBASHI, S., HATA, Y., TANIGUCHI, K., ASARI, K., Biometric System by Foot Pressure Change Based on Neural Network. Multiple–Valued Logic, 2009. ISMVL '09. 39th Int. Symposium, pp. 18–23.
  • [5] ITAKURA F., Minimum prediction residual principle applied to speech recognition, Acoustics, Speech and Signal Processing, Vol. 23, 1975, pp. 67–72.
  • [6] JAIN A. K., FLYNN P., ROSS A. A., Handbook of Biometrics, Springer, 2007.
  • [7] KLEMPOUS R., Diversitas Cybernetica. WKŁ, Warszawa 2005, (in Polish).
  • [8] LEI H., PALLA S, GOVINDARAJU V., ER2: an Intuitive Similarity Measure for On–line Signature Verification. Frontiers in Handwriting Recognition, 2004, pp. 191–195.
  • [9] NISHIUCHI N., KOMATSU S., YAMANAKA K. A biometric identification using the motion of fingers. Proc. of the Int. Conf. on Biometrics and Kansei Engineering. ICBAKE’09, Cieszyn, Poland, 2009, pp. 22–27.
  • [10] PIECHA J., GAŹDZIK T., System Parotec, An Automatic Conclusion Making System Using Feet Konturography, Maschinen und Market, Vogel Verlag – Medien Gruppe, 3–4/1999, pp. 78–80.
  • [11] PIECHA J., ZYGUŁA J., PC Visual Interface For Orthopaedic Expertise, 8–th SCM Int. Conf., Vol. II, Zakopane, Poland, 1995, pp. 162–167.
  • [12] TAKEDA T., TANIGUCHI K., ASARI K, KURAMOTO K, KOBASHI S., HATA Y., Biometric personal authentication by one step foot pressure distribution change by load distribution sensor. Fuzzy Systems, 2009, pp. 906–910.
  • [13] WRÓBEL K., DOROZ R., New signature similarity measure based on average differences. Journal of Medical Informatics & Technologies. Vol. 12, 2008, pp. 51–56.
  • [14] YAMPOLSKIY R.V., GOVINDARAJU V., Behavioural biometrics: a survey and classification. Int. Journal of Biometrics, Vol. 1, Issue 1, 2008, pp. 81–113.
  • [15] ZYGUŁA J., PROKSA R., Neurological pathology classification system based on gait disorders with counter propagation neural network. Int. Conf. on Advanced Simulation of Systems 2008, Czech Republic, 2008, pp. 135–140.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0017-0012
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