COMPUTATIONAL INTELLIGENCE for SHOEPRINT RECOGNITION

M. A. Acevedo Mosqueda, M. A. Acevedo Mosqueda, R. Carreño Aguilera, F. Martinez Zuñiga, D. Pacheco Bautista, M. Patiño Ortiz, W. E.N. Yu

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Abstract

Shoeprint marks present valuable information for forensic investigators to resolve a crime. These marks can be helpful to find the brand of the shoe and can make the investigation easier. In this paper, we present an associative model-based algorithm to match noisy shoeprint patterns with a brand of shoe. The shoeprints are corrupted with additive, subtractive and mixed noises. A particular case of subtractive noise are partial shoeprints such as toe, heel, left-half and right-half prints. The Morphological Associative Memories (MAMs) were applied. Both memories, max and min, recognize noisy shoeprints corrupted with 98% additive and subtractive noise, respectively, with an effectiveness of 100%. The images corrupted with mixed noise were recognized when the additive or subtractive noise applied was greater than the mixed noise; in this case, the recalling was around 70%, otherwise, both memories failed to recognize the shoeprints.

Original languageEnglish
Article number1950080
JournalFractals
Volume27
Issue number4
DOIs
StatePublished - 1 Jun 2019

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Keywords

  • Associative Models
  • Computational Forensics
  • Computational Intelligence
  • Forensic Science
  • Morphological Associative Memories
  • Shoeprint Recognition

Cite this

Acevedo Mosqueda, M. A., Acevedo Mosqueda, M. A., Carreño Aguilera, R., Martinez Zuñiga, F., Pacheco Bautista, D., Patiño Ortiz, M., & Yu, W. E. N. (2019). COMPUTATIONAL INTELLIGENCE for SHOEPRINT RECOGNITION. Fractals, 27(4), [1950080]. https://doi.org/10.1142/S0218348X19500804