A NOVEL METHOD FOR SHOEPRINTS RECOGNITION AND CLASSIFICATION

Min-Quan Jing, Jong Hourm Wei, Ling-Hwei Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

In this paper, we present a method for automatically classifying/recognizing the shoeprint images based on the outsole pattern. Shoeprints are distinctive patterns often found at crime scenes that can provide valuable forensic evidence. Directionality is the most obvious feature in these shoeprints. For extracting features corresponding to the directionality, co-occurrence matrices, Fourier transform, and a directional matrix are applied to the shoeprint image. With the stage of principal component transform, the method is invariant to rotation and translation variance. Experimental results demonstrate the performance of the method.
Original languageEnglish
Title of host publicationPROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6
PublisherIEEE
Pages2846-2851
Number of pages6
ISBN (Print)978-1-4244-4705-3
StatePublished - 2009
Event2009 International Conference on Machine Learning and Cybernetics - Baoding, China
Duration: 12 Jul 200915 Jul 2009

Conference

Conference2009 International Conference on Machine Learning and Cybernetics
CountryChina
CityBaoding
Period12/07/0915/07/09

Keywords

  • Forensic science; Shoeprint; Fourier transforms; Co-occurrence matrix; Principal component transform

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    Jing, M-Q., Wei, J. H., & Chen, L-H. (2009). A NOVEL METHOD FOR SHOEPRINTS RECOGNITION AND CLASSIFICATION. In PROCEEDINGS OF 2009 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-6 (pp. 2846-2851). IEEE.