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Classification of Footwear OutSole Patterns Using Fourier Transform and Local Interest Points

NCJ Number
252388
Date Published
Author(s)
Nicole Richetelli, Mackenzie C. Lee, Carleen A. Lasky, Madison E. Gumjp, Jacqueline A. Speir
Annotation
This research project examined the classification performance of the Fourier–Mellin transform (FMT), phase-only correlation (POC), and local interest points - transformed using Scale Invariant Feature Transform (SIFT) and compared using Random Sample Consensus (RANSAC) - as a function of inputs that included mixed media (blood and dust), transfer mechanisms (gel lifters), enhancement techniques (digital and chemical), and variations in print substrate (ceramic tiles, vinyl tiles and paper).
Abstract
Successful classification of questioned footwear has tremendous evidentiary value; the result can minimize the potential suspect pool and link a suspect to a victim, a crime scene, or even multiple crime scenes to each other. With this in mind, several different automated and semi-automated classification models have been applied to the forensic footwear recognition problem, with superior performance commonly associated with two different approaches: correlation of image power (magnitude) or phase, and the use of local interest points transformed using the Scale Invariant Feature Transform (SIFT) and compared using Random Sample Consensus (RANSAC). Despite the distinction associated with each of these methods, all three have not been cross-compared using a single dataset, of limited quality (i.e., characteristic of crime scene-like imagery), and created using a wide combination of image inputs. The current project addressed this issue. Its results indicate that POC outperforms both FMT and SIFT + RANSAC, regardless of image input (type, quality and totality), and that the difference in stochastic dominance detected for POC is significant across all image comparison scenarios evaluated in this study. (Publisher abstract modified)
Date Created: November 17, 2019