Footwear print retrieval system for real crime scene marks

Authors:
Yi Tang;Sargur N. Srihari;Harish Kasiviswanathan;Jason J. Corso
Affiliations:
Center of Excellence for Document Analysis and Recognition, University at Buffalo, The State University of New York, Amherst, New York;Center of Excellence for Document Analysis and Recognition, University at Buffalo, The State University of New York, Amherst, New York;Center of Excellence for Document Analysis and Recognition, University at Buffalo, The State University of New York, Amherst, New York;Center of Excellence for Document Analysis and Recognition, University at Buffalo, The State University of New York, Amherst, New York
Venue:
IWCF'10 Proceedings of the 4th international conference on Computational forensics
Year:
2010

Citing 8
Cited 1

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IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning

Likelihood ratio estimation in forensic identification using similarity and rarity

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Abstract

Footwear impression evidence has been gaining increasing importance in forensic investigation. The most challenging task for a forensic examiner is to work with highly degraded footwear marks and match them to the most similar footwear print available in the database. Retrieval process from a large database can be made significantly faster if the database footwear prints are clustered beforehand. In this paper we propose a footwear print retrieval system which uses the fundamental shapes in shoes like lines, circles and ellipses as features and retrieves the most similar print from a clustered database. Prints in the database are clustered based on outsole patterns. Each footwear print pattern is characterized by the combination of shape features and represented by an Attributed Relational Graph. Similarity between prints is computed using Footwear Print Distance. The proposed system is invariant to distortions like scale, rotation, translation and works well with the partial prints, color prints and crime scene marks.