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Reseach Article

Full Shoe Print Recognition based on Pass Band DCT and Partial Shoe Print Identification using Overlapped Block Method for Degraded Images

by S. Rathinavel, S.Arumugam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 26 - Number 8
Year of Publication: 2011
Authors: S. Rathinavel, S.Arumugam
10.5120/3126-4301

S. Rathinavel, S.Arumugam . Full Shoe Print Recognition based on Pass Band DCT and Partial Shoe Print Identification using Overlapped Block Method for Degraded Images. International Journal of Computer Applications. 26, 8 ( July 2011), 16-21. DOI=10.5120/3126-4301

@article{ 10.5120/3126-4301,
author = { S. Rathinavel, S.Arumugam },
title = { Full Shoe Print Recognition based on Pass Band DCT and Partial Shoe Print Identification using Overlapped Block Method for Degraded Images },
journal = { International Journal of Computer Applications },
issue_date = { July 2011 },
volume = { 26 },
number = { 8 },
month = { July },
year = { 2011 },
issn = { 0975-8887 },
pages = { 16-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume26/number8/3126-4301/ },
doi = { 10.5120/3126-4301 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:15.503489+05:30
%A S. Rathinavel
%A S.Arumugam
%T Full Shoe Print Recognition based on Pass Band DCT and Partial Shoe Print Identification using Overlapped Block Method for Degraded Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 26
%N 8
%P 16-21
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a novel approach is made to discard the degradations in full shoe prints and partial shoe prints, in processing those images for recognition. A pass band DCT coefficient has been used to extract feature vectors. A more robust approach has been dealt with to find the matching between the partial shoe prints and the images in the data base. This method makes the shoe print recognition process more robust against degradations like noises, orientations and blurred images which are common in shoe print images and also helps in saving the processing time and memory consumption.

References
  1. W. Bodziak, Footwear Impression Evidence Detection, Recovery and Examination, 2000, Second ed. CRC Press.
  2. A. Girod, 1996, Computer classification of the shoeprint of burglars’ shoes, Forensic Science Int. Vol 82, pp 59–65..
  3. M. Phillips, Shoeprint image coding and retrieval system, 1995, Proceedings of the European Convention on Security and Detection, pp. 267-271.
  4. N. Sawyer, “SHOE-FIT: A computerized shoe print database,” 1995, Proc. European Convention on Security and Detection, pp 86-89.
  5. W.Ashley, What shoe was that? the use of computerized image database to assist in identification, Forensic Science Int. 82(1) (1996) 7–20.
  6. S. Mikkonen, T. Astikainenn, Database classification system for shoe sole patterns-identification of partial footwear impression found at a scene of crime, 1994, Journal of Forensic Science 39(5), pp 1227– 1236.
  7. Z. Geradts, J. Keijzer, The image-database REBEZO for shoeprints with developments on automatic classification of shoe outsole designs, 1996, Forensic Science Int. 82, pp 21–31.
  8. A. Alexander, A. Bouridane, D.Crookes, Automatic classification and recognition of shoeprints, 1999, Seventh International IEEE Conference on Image Processing and Its Applications Vol 2 , pp 638–641.
  9. A. Bouridane, A. Alexander, M. Nibouche, D. Crookes, “Application of fractals to the detection and classification of shoeprints,” 2000, IEEE Conference, 0-7803-6297, pp 474–477.
  10. C. Huynh, P. de Chazal, D. McErlean, R. Reilly, T.Hannigan, L.Fleud, Automatic classification of shoeprints for use in forensic science based on the fourier transform, Proc. International Conference on Image Processing 3 , pp 569–572.
  11. P. de Chazal, J. Flynn, R. B. Reilly, Automated processing of shoeprint images based on the fourier transform for use in forensic science, Pattern Analysis and Machine Intelligence, IEEE Transactions on 27 (2005) 341–350.
  12. Matthew Turk and Alex Pentland, “Eigenfaces for Recognition”. Journal of Cognitive Neuroscience, Vol.3, No.1, pp. 71-86, 1991.
  13. P.N.Belhumeur, J.P.Hespanha and D.J. Kriegman, “Eigenfaces versus fisherfaces: Recognition using class specific Linear projection,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.19, No.7, pp. 711-720, Jul.1997.
  14. M. Kirby and L. Sirvoich, “Application of the karhunen-Loeve Procedure for the characterization of human faces, “IEEE Trans. Pattern Anal. Mach. Intell., Vol.12, No.1, pp. 103-108, Jan.1990.
  15. Z.M. Hafed and M.D. Levine, “Face recognition using he discrete cosine transform,” Int. J. Comput. Vis., Vol.43, No.3, pp. 167-188,2001.
  16. J. Lu, K.N. Plataniotis, A.N.Venetsanopoulos, “Face recognition using LDA based algorithms, 2003, IEEE Transactions on Neural Networks 14(1), pp.195-200.
  17. H.Othman and T.Aboulnasr, “A separable low complexity 2D HMM with application to face recognition”, 2003, IEEE Trans. Pattern Anal. Machine Intell., Vol 25, No.10, pp.1229-1238.
  18. Xiao-Yuan Jing and David Zhang , “A Face and Palmprint Recognition Approach Based on Discriminant DCT Feature Extraction,” 2004, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, vol. 34, no. 6, December 2004
  19. Peng-Fei Yu, Dan Xu , “Palmprint Recognition Based On Modified DCT Features and RBF Neural Network,” 2008, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, Kunming, 12-15 July 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Shoe print Fisher Linear Discriminant (FLD) recognition forensic science Principal Component Analysis (PCA)