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- Title
COMPUTATIONAL INTELLIGENCE FOR SHOEPRINT RECOGNITION.
- Authors
ACEVEDO MOSQUEDA, M. E.; ACEVEDO MOSQUEDA, M. A.; CARREÑO AGUILERA, R.; MARTINEZ ZUÑIGA, F.; PACHECO BAUTISTA, D.; PATIÑO ORTIZ, M.; YU, WEN
- 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.
- Subjects
COMPUTATIONAL intelligence; PATTERN matching; NOISE
- Publication
Fractals, 2019, Vol 27, Issue 4, pN.PAG
- ISSN
0218-348X
- Publication type
Article
- DOI
10.1142/S0218348X19500804