Métodos para reconhecimento de íris em ambiente não cooperativo
The identification of humans by their iris structure has been explored since 1993, when the first algorithm was made available by John Daugman. Since then, iris recognition systems are widely used for access control of several kinds of environments. Such systems typically requires the user´s coopera...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Brasil |
| Institución: | Universidade Federal de São Carlos (UFSCAR) |
| Repositorio: | Repositório Institucional da UFSCAR |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufscar.br:20.500.14289/499 |
| Acceso en línea: | https://repositorio.ufscar.br/handle/20.500.14289/499 |
| Access Level: | acceso abierto |
| Palabra clave: | Biometria Reconhecimento de íris à distância Reconhecimento de íris não cooperativo Reconhecimento de padrões Processamento de imagens Biometrics Iris recognition at distance Non-cooperative iris recognition Pattern Recognition CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
| Sumario: | The identification of humans by their iris structure has been explored since 1993, when the first algorithm was made available by John Daugman. Since then, iris recognition systems are widely used for access control of several kinds of environments. Such systems typically requires the user´s cooperation, appropriate lighting conditions, and images obtained in the infra-red band. Dynamic methods for biometric identification has been the subject of studies in the past few years, including iris recognition in non-cooperative environments. This paper proposes a pre-processing methodology to enable iris images classification taken in a noncooperative setting, from users at a certain distance, or while moving. The methodology aims to select images from the visible band containing an acceptable level of noise, and as such being suitable to apply the classification algorithms. Experimental results have shown that images with up to 40% of noise can still be used, suggesting the methodology may be useful as an aid to implement iris recognition systems at distance or in motion. |
|---|