Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/15087
Title: Improving gender classification accuracy in the wild
Authors: Castrilloń-Santana, Modesto 
Lorenzo-Navarro, Javier 
Ramoń-Balmaseda, Enrique 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Gender recognition
LBP
HOG
Issue Date: 2013
Journal: Lecture Notes in Computer Science 
Conference: 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013 
Abstract: 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
Source: 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
Appears in Collections:Actas de congresos
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