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CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
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Automated Processing of Shoeprint Images Based on the Fourier Transform for Use in Forensic Science
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
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Human-Computer Interaction (3rd Edition)
Following the Footsteps of Others: Techniques for Automatic Shoeprint Classification
AHS '07 Proceedings of the Second NASA/ESA Conference on Adaptive Hardware and Systems
Automatic extraction and classification of footwear patterns
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Learning pattern classification-a survey
IEEE Transactions on Information Theory
A Texture Based Shoe Retrieval System for Shoe Marks of Real Crime Scenes
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
LS Footwear Database - Evaluating Automated Footwear Pattern Analysis
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Retrieval of shoemarks using Harris points and sift descriptor
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A texture recognition system of real shoe marks taken from crime scenes
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Automatic drug image identification system based on multiple image features
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
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The rapid and robust identification of a suspect's footwear while he/she is in police custody is an essential component in any system that makes full use of the footwear marks recovered from crime scenes. Footwear is an important source of forensic intelligence and, sometimes, evidence. Here, we present an automated system for shoe model identification from outsole impressions taken directly from suspect's shoes that can provide information in a timely manner, while a suspect is in custody. Currently the process of identifying the shoe model from the 1000s of recorded model types is a time-consuming manual task. The underlying methodology is based on large numbers of localized features located using MSER feature detectors. These features are transformed into robust SIFT descriptors and encoded relative to a feature codebook forming histogram representations of each shoe pattern. This representationist facilitates fast indexing of footwear patterns whilst a finer search proceeds by comparing the correspondence between footwear patterns in a short-list through the application of modified constrained spectral correspondence methods. The effectiveness of this approach is illustrated for a reference dataset of 374 different shoe model patterns, from which 87% first-rank performance and 92% top-eight rank performance are achieved. Practical aspects of the system and future developments are also discussed.