Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/20097
Title: MEG: Multi-Expert Gender classification from face images in a demographics-balanced dataset
Authors: Castrillón-Santana, Modesto 
De Marsico, Maria
Nappi, Michele
Riccio, Daniel
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Recognition
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 
Abstract: In this paper we focus on gender classification from face images, which is still a challenging task in unrestricted scenarios. This task can be useful in a number of ways, e.g., as a preliminary step in biometric identity recognition supported by demographic information.We compare a feature based approach with two score based ones. In the former, we stack a number of feature vectors obtained by different operators, and train a SVM based on them. In the latter, we separately compute the individual scores from the same operators, then either we feed them to a SVM, or exploit likelihood ratio based on a pairwise comparison of their answers.
URI: http://hdl.handle.net/10553/20097
ISBN: 9783319232300
ISSN: 0302-9743
DOI: 10.1007/978-3-319-23231-7_2
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 9279, p. 15-26
Appears in Collections:Actas de congresos
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