Precise eye localization using HOG descriptors
In this paper, we present a novel algorithm for precise eye detection. First, a couple of AdaBoost classifiers trained with Haar-like features are used to preselect possible eye locations. Then, a Support Vector Machine machine that uses Histograms of Oriented Gradients descriptors is used to obtain...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2011 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/58789 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/58789 |
| Access Level: | acceso abierto |
| Palabra clave: | AdaBoost Eye detection HOG Local feature descriptors Commercial software Descriptors Eye localization Eye location Haar-like features Local feature Novel algorithm Pre-selected Algorithms Eye protection Adaptive boosting TEORIA DE LA SEÑAL Y COMUNICACIONES |
| Sumario: | In this paper, we present a novel algorithm for precise eye detection. First, a couple of AdaBoost classifiers trained with Haar-like features are used to preselect possible eye locations. Then, a Support Vector Machine machine that uses Histograms of Oriented Gradients descriptors is used to obtain the best pair of eyes among all possible combinations of preselected eyes. Finally, we compare the eye detection results with three state-of-the-art works and a commercial software. The results show that our algorithm achieves the highest accuracy on the FERET and FRGCv1 databases, which is the most complete comparative presented so far. © Springer-Verlag 2010. |
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