Title: An efficient clustering-based retrieval framework for real crime scene footwear marks

Authors: Yi Tang; Harish Kasiviswanathan; Sargur N. Srihari

Addresses: Department of Computer Science and Engineering, Centre of Excellence for Document Analysis and Recognition, University at Buffalo, The State University of New York, Amherst, New York, 14228, USA. ' Department of Computer Science and Engineering, Centre of Excellence for Document Analysis and Recognition, University at Buffalo, The State University of New York, Amherst, New York, 14228, USA. ' Department of Computer Science and Engineering, Centre of Excellence for Document Analysis and Recognition, University at Buffalo, The State University of New York, Amherst, New York, 14228, USA

Abstract: As the most common type of evidence at crime scenes, footwear marks are found more often than fingerprints, and yet left largely unused due to lack of efficient and reliable tools. While the central task is stated simply - retrieve the closest matches among a database of known outsole prints - the difficulty is the poor quality of the marks and a very large and increasing number of outsole patterns. Since grouping the database into clusters can dramatically speed-up retrieval, we propose clustering based on recurring outsole patterns. The clustered database is used to retrieve similar prints for a given crime scene mark. Geometric shapes like line segments, circles and ellipses are proposed as features for crime scene marks. Then these features are structurally represented in the form of an attributed relational graph (ARG). Robust ARG matching is achieved with the introduced footwear print distance (FPD), a similarity measure for footwear prints. Sensitivity analysis of FPD is performed to show its robustness. The proposed system is invariant to scale, translation, rotation and insensitive to noise and degradations of the prints. Experiments show that the approach outperforms other state-of-the-art footwear print retrieval systems.

Keywords: real crime scenes; footwear marks; similarity measures; image retrieval; clustering; Hough transform; attributed relational graph; ARG; earth mover distance; EMD; shoe prints; ellipse detection; footwear print distance; FPD; footprints; outsole patterns; shoe soles; sole patterns; geometric shapes; geometric features.

DOI: 10.1504/IJGCRSIS.2012.049981

International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2012 Vol.2 No.4, pp.327 - 360

Received: 24 Apr 2012
Accepted: 24 Apr 2012
Published online: 29 Aug 2014 *

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