National Journal of System and Information Technology

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Multiple Feature Extraction for Foot Print Image


     

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In this paper we introduce the new biometric of FOOT PRINT RECOGNITION SYSTEM. This foot image is proved to be distinct for every human being. On the image of the footprint obtained we perform pre-processing. Next we perform the vital step of feature extraction. The best part of this technique is that we use multiple feature extraction techniques. This feature from the foot image is extracted, classified and then recognized. The use of multiple feature extraction will provide us with better accuracy

Keywords

Footprint, Gabor Filter, Wavelet, FNN, SVM
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  • Ye, Syoji Kobashi, Yutaka Hata Kazuhiko Taniguchi Kazunari Asari “ Biometric System by Foot Pressure Change Based on Neural Network”, 2009.

  • V.D.Ambeth Kumar and Dr.M.Ramakrishnan,” Lecagyof Footprint Recognition Computer Applications, Vol 35, No-11, Page 9 9-16,Dec 2011.

  • K. Nakajima, Y. Mizukami, K. Tanaka, and T. Tamura, “Foot-Based Personal Recognition”, IEEE: Tr. On Biomedical Engineering, Vol. 47, No. 11, 2000.

  • Sean W. Yip, B.S. and Thomas E. Prieto, “A System for Force Distribution Measurement Beneath The Feet”, IEEE Conference publication ,pp 32-34, 2004.

  • Pier Luigi Dragotti and Martin Vetterli, “Wavelet Footprint: Theory, Algorithms, and Application”, IEEE Trans.Signal Processing, Vol.51, No.5 May 2003.

  • Jin -Woo Jung and Zeungnam Bien ,” Dynamic-Footprint based Person Identification using Mat-type Pressure Sensor ” IEEE 2003.


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  • Multiple Feature Extraction for Foot Print Image

Abstract Views: 391  |  PDF Views: 1

Authors

Abstract


In this paper we introduce the new biometric of FOOT PRINT RECOGNITION SYSTEM. This foot image is proved to be distinct for every human being. On the image of the footprint obtained we perform pre-processing. Next we perform the vital step of feature extraction. The best part of this technique is that we use multiple feature extraction techniques. This feature from the foot image is extracted, classified and then recognized. The use of multiple feature extraction will provide us with better accuracy

Keywords


Footprint, Gabor Filter, Wavelet, FNN, SVM

References