A Novel Method for Shoeprint Recognition in Crime Scenes

摘要

We present a novel method for shoeprint recognition in crime scenes. First, a preprocessing algorithm is introduced to remove the complicated background, and then Gabor features and Zernike features are extracted and fused to represent the textural and statistical features of shoeprint images. Lastly, a matching approach is also presented to solve the problem of identifying incomplete shoeprints which account for a large proportion in all the captured images. The samples in our database are directly collected from crime scenes. In the experiment, 104 probe shoeprints are tested on a gallery set containing 1,225 shoeprints. Results show that our method is practical and provides better performance in identifying crime scene shoeprint than other algorithms.