Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/15085
Title: Gender classification in large databases
Authors: Ramón-Balmaseda, Enrique 
Lorenzo-Navarro, Javier 
Castrillón-Santana, Modesto 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Gender Recognition
Local Binary Pattern
Large Facial Image Databases
Issue Date: 2012
Journal: Lecture Notes in Computer Science 
Conference: 17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012 
Abstract: 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
Source: 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
Appears in Collections:Actas de congresos
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