TY - JOUR
T1 - COMPUTATIONAL INTELLIGENCE for SHOEPRINT RECOGNITION
AU - Acevedo Mosqueda, M. E.
AU - Acevedo Mosqueda, M. A.
AU - Carreño Aguilera, R.
AU - Martinez Zuñiga, F.
AU - Pacheco Bautista, D.
AU - Patiño Ortiz, Miguel
AU - Yu, W. E.N.
N1 - Publisher Copyright:
© 2019 The Author(s).
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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.
KW - Associative Models
KW - Computational Forensics
KW - Computational Intelligence
KW - Forensic Science
KW - Morphological Associative Memories
KW - Shoeprint Recognition
UR - http://www.scopus.com/inward/record.url?scp=85068843636&partnerID=8YFLogxK
U2 - 10.1142/S0218348X19500804
DO - 10.1142/S0218348X19500804
M3 - Artículo
AN - SCOPUS:85068843636
SN - 0218-348X
VL - 27
JO - Fractals
JF - Fractals
IS - 4
M1 - 1950080
ER -
