Abstract
Identification of the footwear traces from crime scenes is an important yet largely forgotten aspect of forensic intelligence and evidence. We present initial results from a developing automatic footwear classification system. The underlying methodology is based on large numbers of localized features located using MSER feature detectors. These features are transformed into robust SIFT or GLOH descriptors with the ranked correspondence between footwear patterns obtained through the use of constrained spectral correspondence methods. For a reference dataset of 368 different footwear patterns, we obtain a first rank performance of 85% for full impressions and 84% for partial impressions.
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Data taken from the UK National Shoewear Database, Forensic Science Service, Birmingham, B37 7YN, UK
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© 2006 Springer-Verlag Berlin Heidelberg
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Pavlou, M., Allinson, N.M. (2006). Automatic Extraction and Classification of Footwear Patterns. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_87
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DOI: https://doi.org/10.1007/11875581_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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