Um método de reconhecimento de indivíduos por geometria da mão

Detalhes bibliográficos
Ano de defesa: 2015
Autor(a) principal: Nascimento, Márcia Valdenice Pereira do
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal da Paraíba
Brasil
Informática
Programa de Pós-Graduação em Informática
UFPB
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/7848
Resumo: Over the past few years, recognition by biometric information has been increasingly adopted in several applications, including commerce, government and forensics. One reason for this choice is based on the fact that biometric information is more difficult to falsify, share, hide or misplace than other alternatives like ID cards and passwords. Many characteristics of the individual (physical or behavioral) can be used in a biometric system, such as fingerprint, face, voice, iris, gait, palmprint, hand geometry, and others. Several researches have explored these and other features producing safer and more accurate recognition methods, but none of them are completely fault tolerant and there is still much to evolve and improve in this area. Based on this, this work presents a new approach to biometric recognition based on hand geometry. A database with 100 individuals and with samples of both sides of the hands was used. The feature extraction process prioritizes user comfort during capture and produces segmentation of hands and fingers with high precision. Altogether, 84 features have been extracted from each individual and the method was evaluated from different classification and verification approaches. Classification tests using cross-validation and stratified random subsampling techniques were performed. The experiments demonstrated competitive results when compared to other state-of-the-art methods with hand geometry. The proposed approach obtained with 100% accuracy in different classification strategies and EER rate of 0.75% in the verification process.