Forensic analysis of microtraces using image recognition through machine learning

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorNational Institute of Forensic Science and Technology (INCT Forense)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorBrasilia-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorRodrigues, Caio Henrique Pinke-
Autor(es): dc.creatorSousa, Milena Dantas da Cruz-
Autor(es): dc.creatordos Santos, Michele Avila-
Autor(es): dc.creatorFilho, Percio Almeida Fistarol-
Autor(es): dc.creatorVelho, Jesus Antonio-
Autor(es): dc.creatorLeite, Vitor Barbanti Pereira-
Autor(es): dc.creatorBruni, Aline Thais-
Data de aceite: dc.date.accessioned2025-08-21T19:23:50Z-
Data de disponibilização: dc.date.available2025-08-21T19:23:50Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.microc.2024.111780-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/300883-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/300883-
Descrição: dc.descriptionTraces found at a crime scene can be interpreted as vectors of information that help describe the possible dynamics of the crime. However, some analyses show that pattern recognition, especially in materials, is subjective, as it depends on the analyst's references. Based on this problem, the work aimed to use different statistical methods to establish pattern recognition in silver tapes. For this, two approaches were used, one based on deep learning (convolutional neural networks) and the other using a different method (Pearson's correlation, distance metrics, and Principal Component Analysis). The dataset comprised four brands of silver-tape available in the retail market in the crime scene region, and fragments of tape originating after the detonation of a handmade explosive device. These materials were analyzed using a Leica DVM6 microscope. In both approaches, it was possible to recognize patterns. In deep learning, it was possible to establish that the fragments came from a common origin. The best model demonstrated that 92.1 % of the real materials questioned were the same silver-tape, with a confidence level of 0.94. By combining the methods, it was possible to observe a trend among the results. These responses demonstrated that using images in a computer vision context could remove the subjectivity of forensic analysis and correlate the microtraces found at a crime scene. In this way, these techniques can open new perspectives for the forensic area, making the interpretation more objective and transparent in its responses to society.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionNational Institute of Forensic Science and Technology (INCT Forense), Ribeirão Preto-
Descrição: dc.descriptionDepartment of Chemistry Ribeirão Preto School of Philosophy Sciences and Letters University of São Paulo, Ribeirão Preto-
Descrição: dc.descriptionFederal Police Brasilia, Federal District-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences São Paulo State University “Júlio Mesquita Filho” São José do Rio Preto-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences São Paulo State University “Júlio Mesquita Filho” São José do Rio Preto-
Descrição: dc.descriptionCAPES: Financial Code 001-
Idioma: dc.languageen-
Relação: dc.relationMicrochemical Journal-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectComputer vision-
Palavras-chave: dc.subjectConvolutional Neural Network-
Palavras-chave: dc.subjectEvidence-
Palavras-chave: dc.subjectForensic Science-
Palavras-chave: dc.subjectStatistical analysis-
Título: dc.titleForensic analysis of microtraces using image recognition through machine learning-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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