Gender classification in large databases

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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