Classificação e Verificação Multibiométrica por Geometria da Mão e Impressão Palmar com Otimização por Algoritmos Genéticos

• Autor(a) principal: Silva, Arnaldo Gualberto de Andrade e
• Data de Publicação: 2015
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UFPB
• Texto Completo: https://repositorio.ufpb.br/jspui/handle/tede/7857
• topic: Biometria, Geometria da Mão, Impressão Palmar, Algoritmos Genéticos, Verificação, Classificação; Biometrics, Hand Geometry, Palmprint, Genetic Algorithms, Verification, Classification; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO

Biometrics provides a trusted authentication mechanism by using traits (physical or behavioral) which identify users based on their natural characteristics. Biometric services of classification and verification of users are considered, in principle, more secure than password-based systems, requiring the presentation of a unique physical characteristic and, therefore, the presence of the user at least in the moment of authentication. However, such methods have vulnerabilities that result in high rates of false verification, even in the most modern systems. On the other hand, Genetic algorithms (GA) are an optimization approach based on the principle of natural selection proposed by Charles Darwin which has been proving to be a useful tool in finding solutions to complex problems. Moreover, the use of genetic algorithms in biometrics systems has also been growing as they are an interesting alternative for selecting features. This work applies a genetic algorithm-based approach to optimizing parameters of classification and verification of a hand dataset. The BioPass-UFPB multi-biometric dataset is presented and used to test and validate the proposed method. In total, 99 features – 85 geometric features and 14 texture features - extracted from each hand image were used. Additionally, the importance of each feature is also analyzed. The results showed relative improvements of EER greater than 30% and 90% in the best cases of the two verification approaches performed, respectively. As for classification, the use of genetic algorithms were able to reduce, on average, the number of templates to be recovered by the system to ensure that at least one of these is of the same class of the reference sample. In conclusion, both the results showed and the BioPass-UFPB dataset might help the development of new hand geometry-based biometric recognition systems.