Una gramática visual para la detección de rostros
Several methods for face detection have been developed with certain success, these methods typically include features like texture, skin color, some predefined templates or deformable templates, etc., however these tend to fail under “difficult” conditions such as partial occlusions and changes in o...
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| Formato: | tesis de maestría |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2011 |
| País: | México |
| Recursos: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repositorio: | Repositorio Institucional del INAOE |
| Idioma: | español |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/703 |
| Acesso em linha: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/703 |
| Access Level: | acceso abierto |
| Palavra-chave: | info:eu-repo/classification/Redes bayesianas/Bayesian networks info:eu-repo/classification/Gramáticas/Grammars info:eu-repo/classification/Reconocimiento facial/Face recognition info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Resumo: | Several methods for face detection have been developed with certain success, these methods typically include features like texture, skin color, some predefined templates or deformable templates, etc., however these tend to fail under “difficult” conditions such as partial occlusions and changes in orientation and illumination. We propose a novel technique for face detection based on a visual grammar. We first define a symbol relational grammar for faces, representing the visual elements of a face and their spatial relations. This grammar is then transformed to a Bayesian network representation. The structure of the Bayesian network is derived from the grammar, and its parameters are obtained from data, i.e., from positive and negative examples of faces. Then the Bayesian network is used for face detection via probabilistic inference, using as evidence a set of weak detectors for different face components. We evaluated our method on a set of sample images of faces under “difficult” conditions, and contrasted it with a simplified model without spatial relationships, and the AdaBoost face detector. The results show a significant improvement when using our method based on a visual grammar. Although the grammar is restricted to the representation of faces, it is possible to extend it to represent a complete person or other object. |
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