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http://hdl.handle.net/10553/15085
| Título: | Gender classification in large databases | Autores/as: | Ramón-Balmaseda, Enrique Lorenzo-Navarro, Javier Castrillón-Santana, Modesto |
Clasificación UNESCO: | 120304 Inteligencia artificial | Palabras clave: | Gender Recognition Local Binary Pattern Large Facial Image Databases |
Fecha de publicación: | 2012 | Publicación seriada: | Lecture Notes in Computer Science | Conferencia: | 17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012 | Resumen: | In this paper, we address the challenge of gender classi - cation using large databases of images with two goals. The rst objective is to evaluate whether the error rate decreases compared to smaller databases. The second goal is to determine if the classi er that provides the best classi cation rate for one database, improves the classi cation results for other databases, that is, the cross-database performance. | URI: | http://hdl.handle.net/10553/15085 | ISBN: | 9783642332746 | ISSN: | 0302-9743 | DOI: | 10.1007/978-3-642-33275-3_9 | Fuente: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 7441 LNCS, p. 74-81 |
| Colección: | Actas de congresos |
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