NCJ Number
246091
Journal
Journal of Forensic Sciences Volume: 58 Issue: 3 Dated: May 2013 Pages: 625-630
Date Published
May 2013
Length
6 pages
Annotation
In this study, scale-invariance feature transform is proposed and evaluated for recognition and retrieval of partial and noisy shoeprint images.
Abstract
Shoeprints left at the crime scene provide valuable information in criminal investigation due to the distinctive patterns in the sole. Those shoeprints are often incomplete and noisy. In this study, scale-invariance feature transform is proposed and evaluated for recognition and retrieval of partial and noisy shoeprint images. The proposed method first constructs different scale spaces to detect local extrema in the underlying shoeprint images. Those local extrema are considered as useful key points in the image. Next, the features of those key points are extracted to represent their local patterns around key points. Then, the system computes the cross-correlation between the query image and each shoeprint image in the database. Experimental results show that full-size prints and prints from the toe area perform best among all shoeprints. Furthermore, this system also demonstrates its robustness against noise because there is a very slight difference in comparison between original shoeprints and noisy shoeprints. Abstract published by arrangement with John Wiley & Sons.