Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/15087
Título: Improving gender classification accuracy in the wild
Autores/as: Castrilloń-Santana, Modesto 
Lorenzo-Navarro, Javier 
Ramoń-Balmaseda, Enrique 
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Gender recognition
LBP
HOG
Fecha de publicación: 2013
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013 
Resumen: In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Diff erent descriptors, resolutions and classfii ers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.
URI: http://hdl.handle.net/10553/15087
ISBN: 9783642418266
ISSN: 0302-9743
DOI: 10.1007/978-3-642-41827-3_34
Fuente: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 8259 LNCS, p. 270-277
Colección:Actas de congresos
miniatura
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