Automated Processing of Shoeprint Images Based on the Fourier Transform for Use in Forensic Science
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thresholding of noisy shoeprint images based on pixel context
Pattern Recognition Letters
Shoeprint Image Retrieval Based on Local Image Features
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
Shoeprint Image Retrieval by Topological and Pattern Spectra
IMVIP '07 Proceedings of the International Machine Vision and Image Processing Conference
Automatic extraction and classification of footwear patterns
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Local-feature-based image retrieval with weighted relevance feedback
International Journal of Business Intelligence and Data Mining
| Hi-index | 0.01 |
Shoe marks at the place of crime provide valuable forensic evidence. This paper presents a technique for rotation and intensity invariant automatic shoeprint matching. Multiresolution features of a shoeprint have been extracted using Gabor transform. Rotation of the shoeprint image has been estimated using Radon transform and is compensated by rotating the features in opposite direction. The performance of the proposed algorithm has been compared with the technique in which the features have been determined using Fourier transform and its power spectral density. Shoeprint database has been generated by inviting participants to tread on an inkpad and then stamp on a piece of paper. Euclidian distance classifier has been used to find a suitable match. The performance of the proposed algorithm has been evaluated in terms of correct recognition rate computed using best match score at rank '1' and cumulative match score for the first four matches with rotation, intensity and/or mixed attacks. A good matching performance has been achieved with rotation attack; typically 91 percent at rank '1' and 100 percent at rank '2' for full prints. Performance of the proposed technique is better even for partial shoeprints. Experimentation has also been carried out by perturbing shoeprint images with Gaussian white noise, salt and pepper noise to evaluate the robustness of the proposed technique.