Forensic palynology: computer vision and geotechnologies to support criminalistics expertise

• Autor(a) principal: Gonçalves, Ariadne Barbosa
• Data de Publicação: 2022
• Outros Autores: Albuquerque, Pedro Lucas França, Alves, Rodolfo de França, Astolfi, Gilberto, Borges, Felipe Silveira Brito, Carmona, Milena dos Santos, Cereda, Marney Pascoli, Chaves, Sergio Augusto de Miranda, Ferreira, Alessandro dos Santos, Godoi, Raquel de Faria, Menezes, Geazy Vilharva, Oliveira, Wedney Rodolpho de, Paranhos Filho, Antonio Conceição, Pott, Arnildo, Reinhard, Karl Jan, Santos, Francisco de Assis Ribeiro dos, Su, Hongbo, Pistori, Hemerson
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/30422
• topic: Aprendizagem Automática; Geoprocessamento; Homicídio.; Machine Learning; Geoprocessing; Homicide.; Aprendizaje automático; Geoprocesamiento; Asesinato.

Pollen grains can provide valuable information to forensic palynology, such as the time of death or the possible origin of a corpse. Forensic Palynology is a vital tool to be used in a criminal investigation because the different environment has distinct pollen signatures. Brazil has a rich and diversified flora that is suitable for the application of forensic palynology. The purpose of this research is to introduce palynology automation as a tool to improve the investigative method in forensic palynology and apply it to forensic palynology automation. The studied city has different vegetation types, in which we performed assessments to identify its correspondent pollen types. PALINOVIC algorithm was developed using computer vision and geotechnology techniques. Our results show that it is possible to correlate pollen grains found in forensic samples by automatic pollen identification and with a mapping of the likely vegetation. Our results show that it is possible to relate the presence of pollen grains found in forensic samples through the automatic identification of images together with a database of georeferenced plant species. It was possible to analyze the pollen grains collected in eight bodies, where the algorithm presented a performance of 90.51% in the pollen grain classification tests. Furthermore, pollen grains could be correlated with the type of vegetation where the body was found. Thus, the technique developed can be applied in other urban centers from a previous georeferencing of plants, as well as a pollen database.