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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Pavlou, M., & Allinson, N. M. (2006). Automatic extraction and classification of footwear patterns. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4224 LNCS, pp. 721–728). Springer Verlag. https://doi.org/10.1007/11875581_87
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