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

Resultado de la investigación: Contribución a una revistaArtículo

1 Cita (Scopus)

Resumen

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.

Idioma originalInglés
Número de artículo1950080
PublicaciónFractals
Volumen27
N.º4
DOI
EstadoPublicada - 1 jun 2019

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    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