Reconhecimento semiautomático de dentes para a identificação humana forense
Arquivos
Data
2011-11-25
Autores
Barboza, Elizabeth Bonsaglia [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Estadual Paulista (Unesp)
Resumo
Na sociedade atual, a identificação exata e rápida dos indivíduos é uma necessidade. Nas aplicações forenses, a identificação por meio de características biométricas é bastante usual e muitas vezes é a única alternativa. A arcada dentária é uma das mais importantes e populares características biométricas utilizadas pela Odontologia Legal na área forense. O principal objetivo da Odontologia Legal é identificar indivíduos falecidos para os quais os outros meios de identificação biométrica (impressões digitais, faces, etc.) não são possíveis de serem aplicados. Em geral, o perito humano realiza a comparação manual entre registros dentários antemortem e postmortem anotando as diferenças encontradas em cada dente, o que demanda muito tempo e torna essa prática suscetível a erros, daí a importância do desenvolvimento de sistemas automáticos ou semiautomáticos para identificação humana forense. O objetivo dessa dissertação de mestrado é avaliar métodos baseados na Transformada Imagem Floresta para segmentação semiautomática de dentes e suas restaurações em imagens de radiografias panorâmicas de arcadas dentárias, utilizando os contornos obtidos para definir descritores biométricos para a identificação humana forense. Os descritores de formas baseados nos métodos Contexto da Forma (Shape Context) e Estatística dos Ângulos de Raios (BAS – Beam Angle Statistics) foram implementados e avaliados para o reconhecimento dos dentes. A técnica da Distância de Edição avaliou o reconhecimento das restaurações baseada nos códigos dentais gerados. Resultados experimentais obtidos sobre uma base de 40 imagens de radiografias de arcadas dentárias, contendo no total 1126 imagens de dentes, mostram que o método de segmentação baseado na Transformada Imagem Floresta Diferencial...
Nowadays, the fast and accurate individual’s identification is a great necessity. In forensic applications, identification through biometric features is often usual and in some cases is the only alternative. The dental arch is one of the most important and popular biometric characteristic used in the dental forensic field. The primary goal of forensic dentistry is to identify deceased individuals for whom other forms of biometric identification (fingerprints, faces, etc.) are not able to be applied. In general, the human expert performs the manual comparison between antemortem and postmortem dental records noting the differences found in each tooth, which requires much time and practice, but this manual practice is more susceptible to errors. Hence, the importance of the development of automatic or semiautomatic forensic human identification systems. The goal of this Master dissertation is to evaluate methods based on Image Forest Transform for semiautomatic segmentation of teeth and their restorations in panoramic radiographs images of dental arches, using the contours obtained to define biometric descriptors for forensic human identification. The shape descriptors based on the Shape Context method and Beam Angle Statistics (BAS) were implemented and evaluated for the teeth recognition. The Edition Distance technique was used to evaluate the restorations and all dental work through the generated dental codes. Experimental results obtained on a database of 40 radiographs images, containing a total of 1126 teeth images, show that the segmentation method based on Differential Image Foresting Transform presented good and promising results for the semiautomatic teeth segmentation. They also showed that the Shape Context and BAS descriptors had similar results, with a slight advantage for the... (Complete abstract click electronic access below)
Nowadays, the fast and accurate individual’s identification is a great necessity. In forensic applications, identification through biometric features is often usual and in some cases is the only alternative. The dental arch is one of the most important and popular biometric characteristic used in the dental forensic field. The primary goal of forensic dentistry is to identify deceased individuals for whom other forms of biometric identification (fingerprints, faces, etc.) are not able to be applied. In general, the human expert performs the manual comparison between antemortem and postmortem dental records noting the differences found in each tooth, which requires much time and practice, but this manual practice is more susceptible to errors. Hence, the importance of the development of automatic or semiautomatic forensic human identification systems. The goal of this Master dissertation is to evaluate methods based on Image Forest Transform for semiautomatic segmentation of teeth and their restorations in panoramic radiographs images of dental arches, using the contours obtained to define biometric descriptors for forensic human identification. The shape descriptors based on the Shape Context method and Beam Angle Statistics (BAS) were implemented and evaluated for the teeth recognition. The Edition Distance technique was used to evaluate the restorations and all dental work through the generated dental codes. Experimental results obtained on a database of 40 radiographs images, containing a total of 1126 teeth images, show that the segmentation method based on Differential Image Foresting Transform presented good and promising results for the semiautomatic teeth segmentation. They also showed that the Shape Context and BAS descriptors had similar results, with a slight advantage for the... (Complete abstract click electronic access below)
Descrição
Palavras-chave
Biometria, Processamento de imagens - Tecnicas digitais, Odontologia legal, Dentes - Identificação, Biometrics, Forensic human identification, Teeth recognition
Como citar
BARBOZA, Elizabeth Bonsaglia. Reconhecimento semiautomático de dentes para a identificação humana forense. 2011. 95 f. Dissertação (mestrado) - Universidade Estadual Paulista, Instituto de Biociências, Letras e Ciências Exatas, 2011.