Robust Shoeprint Retrieval Method Based on Local-to-Global Feature Matching for Real Crime Scenes

• Cui, Junjian ^[1] ; Zhao, Xiaorui ^[1] ; Liu, Nini ^[1] ; Morgachev, Sergey ^[1] ; Li, Daixi ^[1]
  1. [1] Dalian Everspry Sci & Tech Co., Ltd.,
• Localización: Journal of forensic sciences, ISSN-e 1556-4029, ISSN 0022-1198, Vol. 64, Nº. 2, 2019, págs. 422-430
• Idioma: inglés
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• Resumen

  • In this study, an automatic and robust crime scene shoeprint retrieval method is proposed. As most shoeprints left at crime scenes are randomly partial and noisy, crime scene shoeprint retrieval is a challenging task. 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 study, 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.