Fusion of holistic and part based features for gender classification in the wild
Gender classi cation (GC) in the wild is an active area of current research. In this paper, we focus on the combination of a holistic state of the art approach based on features extracted from the facial pattern, with patch based approaches that focus on inner facial areas. Those regions are selecte...
| Autores: | , , |
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| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2015 |
| País: | España |
| Repositorio: | accedaCRIS portal de investigación de la Universidad de las Palmas de Gran Canaria |
| OAI Identifier: | oai:accedacris.ulpgc.es:10553/20096 |
| Acceso en línea: | http://hdl.handle.net/10553/20096 |
| Access Level: | acceso abierto |
| Palabra clave: | 120304 Inteligencia artificial Recognition Patterns Gender classification Local descriptors Score level fusion |
| Sumario: | Gender classi cation (GC) in the wild is an active area of current research. In this paper, we focus on the combination of a holistic state of the art approach based on features extracted from the facial pattern, with patch based approaches that focus on inner facial areas. Those regions are selected for being relevant to the human system according to the psychophysics literature: the ocular and the mouth areas. The resulting proposed GC system outperforms previous approaches, reducing the classi cation error of the holistic approach roughly a 30%. |
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