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Threshold Value Determination for Recognition of Partial Shoe Prints for Forensic Analysis

K. Anusudha

Abstract


This paper aims to develop an automated system to aid forensic scientists in rapid identification of the partial shoe print images based on Phase only Correlation technique. The proposed new technique captures more discriminative information when compared to amplitude based methods. The main advantage of this technique is the capability to match a low quality shoe print image accurately and efficiently. The Cross-Phase Spectrum function coupled with the Spectral Weighting function results in high performance and increased recognition rate method. The proposed algorithm was checked on real time images obtained on various test beds. To check the efficiency of the algorithm the percentage of matching between the reference image and the sample image were performed.

Keywords


Phase Only Correlation, Gaussian Function, Spectral Weighing Function, Laplacian of Gaussian (LOG) Function.

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References


M. Gueham, A. Bouridane and D. Crookes,“Automatic Recognitionof Partial Shoeprints Based on Phase-Only Correlation,” Proc. Int‟l Conf. Image-ICIP-2007.

Z. Geradts and J. Keijzer, “The image data REBEZO for shoeprint with developments for automatic classification of shoe outsole designs,” Forensic Science Int‟l, vol. 82, pp. 21-31, 1996.

A. Alexander, A. Bouridane, and D. Crookes, “Automatic Classification and Recognition of Shoeprints,” Proc. Seventh Int‟l Conf. Image Processing and Its Applications, vol. 2, pp. 638-641, 1999.

A. Bouridane, A. Alexander, M. Nibouche, and D. Crookes,“Application of Fractals to the Detection and Classification of Shoeprints,” Proc. 2000 Int‟l Conf. Image Processing, vol. 1, pp.474-477, 2000.

P. D. Chazal, J. Flynn, and R.B. Reilly, “Automated processing of shoeprint images based on the Fourier transform for use in forensic science,” IEEE Trans. Pattern Analy. Machine Intell., vol. 27, no. 3, pp. 341-350, Mar. 2005.

W.J. Bodziak, ”Footwear Impression Evidence Detection, Recovery and Examination”, second ed. CRC Press, 2000.

A.Girod, “Presentation at the European Meeting for Shoeprint Tool mark Examiners,” 1997.

A. Girod, “Computer Classification of the Shoeprint of Burglar Soles,” Forensic Science Int‟l, vol. 82, pp. 59-65, 1996.

N. Sawyer, “‟SHOE-FIT‟ A Computerised Shoe Print Database,” Proc. European Convention on Security and Detection, pp. 86-89, May 1995.

W. Ashley, “What Shoe Was That? The Use of Computerised Image Database to Assist in Identification, “Forensic Science Int‟l, vol.82, pp. 67-79, 1996.

A.V. Oppenheim and J.S. Lim, “The importance of phase in signals,” IEEE Proc., vol. 69, no. 5, pp. 529-541, 1981.

K. Takita, T. Aoki, Y. Sasaki, T. Higuchi, and K. Kobayashi,“High-accuracy subpixel image registration based on phase-only correlation,” IEICE Trans. Fundamentals, vol. E86-A, no. 8, pp.1925-1934, Aug. 2003

GONZALEZ R C and WINTZ P, 1989, “Digital Image Processing” Addison Wesley, USA.

Chazal, P., Flynn, J., Reilly, R.B.: Automated processing of shoeprint images based on the Fourier transform for use in forensic science. IEEE Trans. Pattern Anal. Mach. Intell 27 (2005) 341-350.

Zhang, L., Allinson, N.: Automatic shoeprint retrieval system for use in forensic investigations. In: UK Workshop On Computational Intelligence. (2005).

W., Taniar, D., Torabi, T.: Image mining: A case for clustering shoe prints.International Journal of Information Technology and Web Engineering 3 (2008) 70-84.

AlGarni, G., Hamiane, M.: A novel technique for automatic shoeprint image retrieval.Forensic Science International 181 (2008) 10-14.

Xiao, R., Shi, P.: Computerized matching of shoeprints based on sole pattern. Lecture Notes In Computer Science;Proceedings of the 2nd international workshop on Computational Forensics 5158 (2008) 96-104.

Jingl, M.Q., Ho, W.J., Chen, L.H.: A novel method for shoeprints recognition and classification. International Conference on Machine Learning and Cybernetics 5 (2009) 2846-2851.

Nibouche, O., Bouridane, A., Gueham, M., Laadjel, M.: Rotation invariant matching of partial shoeprints. International Machine Vision and Image Processing Conference (2009) 94-98.

Dardi, F., Cervelli, F., Carrato, S.: A texture based shoe retrieval system for shoe marks of real crime scenes. Proc. International Conference on Image Analysis and Processing 5716 (2009) 384-393

Cervelli, F., Dardi, F., Carrato, S.: Comparison of footwear retrieval systems for synthetic and real shoe marks. In: Proc. ISA'09 6th Intl. Symp. Image and Signal Processing and Analysis, Salzburg, Austria. (2009) 684-689

Dardi, F., Cervelli, F., Carrato, S.: A combined approach for footwear retrieval of crime scene shoe marks. Proc. ICDP-09, Third International Conference on Imaging for Crime Detection and Prevention, London, UK (2009) Paper No. P09

Gueham, M., Bouridane, A., Crookes, D.: Automatic classi_cation of partial shoeprints using advanced correlation _lters for use in forensic science. International Conferenceon Pattern Recognition (2008)1-4

Patil, P.M., Kulkarni, J.V.: Rotation and intensity invariant shoeprint matching using gabor transform with application to forensic science. Pattern Recognition 42 (2009) 1308-1317.


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