Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/17812
Title: Multi-scale score level fusion of local descriptors for gender classification in the wild
Authors: Castrillón-Santana, Modesto 
Lorenzo Navarro, José Javier 
Ramón Balmaseda, Enrique José 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Soft biometrics
Gender classification
Local descriptors
Score level fusion
CNN
Issue Date: 2017
Journal: Multimedia Tools and Applications 
Abstract: The 2015 FRVT gender classification (GC) report evidences the problems that current approaches tackle in situations with large variations in pose, illumination, background and facial expression. The report suggests that both commercial and research solutions are hardly able to reach an accuracy over 90% for The Images of Groups dataset, a proven scenario exhibiting unrestricted or in the wild conditions. In this paper, we focus on this challenging dataset, stepping forward in GC performance by observing: 1) recent literature results combining multiple local descriptors, and 2) the psychophysics evidences of the greater importance of the ocular and mouth areas to solve this task...
URI: http://hdl.handle.net/10553/17812
ISSN: 1380-7501
DOI: 10.1007/s11042-016-3653-2
Source: Multimedia Tools and Applications [ISSN 1380-7501], v. 76 (4), p. 4695–4711
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