Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/15077
Title: A performance based study on gender recognition in large datasets
Authors: Díaz Cabrera, Moisés 
Lorenzo Navarro, José Javier 
Castrillón-Santana, Modesto 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Gender recognition
BEFIT
Classiffier fusion
LFW
MORPH
Issue Date: 2012
Abstract: Gender recognition has achieved impressive results based on the face appearance in controlled datasets. Its application in the wild and large datasets is still a challenging task for researchers. In this paper, we make use of classical techniques to analyze their performance in controlled and uncontrolled condition respectively with the LFW and MORPH datasets. For both sets the benchmarking protocol follows the 5-fold cross-validation proposed by the BEFIT challenge.
URI: http://hdl.handle.net/10553/15077
Source: VI Jornadas de Reconocimiento Biométrico de Personas (JRBP12). Las Palmas de Gran Canaria. 2012
Appears in Collections:Actas de congresos
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