MEG: Multi-Expert Gender classification from face images in a demographics-balanced dataset
In this paper we focus on gender classification from face images, which is still a challenging task in unrestricted scenarios. This task can be useful in a number of ways, e.g., as a preliminary step in biometric identity recognition supported by demographic information.We compare a feature based ap...
| Autores: | , , , |
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| Tipo de documento: | capítulo de livro |
| Data de publicação: | 2015 |
| País: | España |
| Repositório: | accedaCRIS portal de investigación de la Universidad de las Palmas de Gran Canaria |
| OAI Identifier: | oai:accedacris.ulpgc.es:10553/20097 |
| Acesso em linha: | http://hdl.handle.net/10553/20097 |
| Access Level: | Acceso aberto |
| Palavra-chave: | 120304 Inteligencia artificial Recognition |
| Resumo: | In this paper we focus on gender classification from face images, which is still a challenging task in unrestricted scenarios. This task can be useful in a number of ways, e.g., as a preliminary step in biometric identity recognition supported by demographic information.We compare a feature based approach with two score based ones. In the former, we stack a number of feature vectors obtained by different operators, and train a SVM based on them. In the latter, we separately compute the individual scores from the same operators, then either we feed them to a SVM, or exploit likelihood ratio based on a pairwise comparison of their answers. |
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