Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/20096
Title: Fusion of holistic and part based features for gender classification in the wild
Authors: Castrillón-Santana, Modesto 
Lorenzo-Navarro, Javier 
Ramón-Balmaseda, Enrique 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Recognition
Patterns
Issue Date: 2015
Journal: Lecture Notes in Computer Science 
Conference: 18th International Conference on Image Analysis and Processing (ICIAP) 
18th International Conference on Image Analysis and Processing, ICIAP 2015 BioFor, CTMR, RHEUMA, ISCA, MADiMa, SBMI, and QoEM 
Abstract: Gender classi cation (GC) in the wild is an active area of current research. In this paper, we focus on the combination of a holistic state of the art approach based on features extracted from the facial pattern, with patch based approaches that focus on inner facial areas. Those regions are selected for being relevant to the human system according to the psychophysics literature: the ocular and the mouth areas. The resulting proposed GC system outperforms previous approaches, reducing the classi cation error of the holistic approach roughly a 30%.
URI: http://hdl.handle.net/10553/20096
ISBN: 9783319232218
ISSN: 0302-9743
DOI: 10.1007/978-3-319-23222-5_6
Source: Murino V., Puppo E., Sona D., Cristani M., Sansone C. (eds) New Trends in Image Analysis and Processing -- ICIAP 2015 Workshops. ICIAP 2015. Lecture Notes in Computer Science, vol 9281. Springer, Cham
Appears in Collections:Actas de congresos
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