Education

Robust Shoeprint Retrieval Method Based on Local-to-Global Feature Matching for Real Crime Scenes
Abstract
In this paper, an automatic and robust crime scene shoeprint retrieval method is proposed. Since most shoeprints left at crime scenes are randomly partial and noisy, crime scene shoeprint retrieval is a challenging task. In order to handle partial, noisy shoeprint images, we employ denoising deep belief network (DBN) to extract local features and use spatial pyramid matching (SPM) to obtain a local-to-global matching score. In this paper, 536 query shoeprint images from crime scenes and a large scale database containing 34,768 shoeprint images are used to evaluate the retrieval performance. Experimental results show that the proposed method outperforms other state-of-the-art methods in terms of retrieval accuracy, feature dimension, and retrieval speed. The proposed method achieves a cumulative match score (CMS) of 65.67% at top 10 which is 5.60% higher than the second best performing method.
Please follow the link below to read full version.
https://onlinelibrary.wiley.com/doi/full/10.1111/1556-4029.13894

An Automatic Shoeprint Retrieval Method Using Neural Codes for Commercial Shoeprint Scanners
Abstract
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.
Please follow the link below to read full version.
https://link.springer.com/chapter/10.1007/978-981-10-7302-1_14