An Automatic Shoeprint Retrieval Method Using Neural Codes for Commercial Shoeprint Scanners

In this paper, an automatic shoeprint retrieval method used in forensic science is proposed. The proposed method extracts shoeprint features using recently reported descriptor called neural code. The first step of feature extraction is rotation compensation. Then, shoeprint image is divided into top region and bottom region, and two neural codes for both regions are obtained. Afterwards, a matching score between test image and reference image is calculated. The matching score is a weighted sum of cosine similarities of both regions' neural codes. Experimental results show that our method outperforms other methods on a large-scale database captured by commercial shoeprint scanners. By using PCA, the performance can be improved while the feature dimension is reduced dramatically. To our knowledge, this is the first study using the database collected by commercial shoeprint scanners, and our method obtained a cumulative match score of 88.7% at top 10.