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 |
SCOPUSTM
Citations
2
checked on Feb 21, 2021
Page view(s)
42
checked on Feb 21, 2021
Download(s)
71
checked on Feb 21, 2021
Google ScholarTM
Check
Altmetric
Share
Export metadata
This item is licensed under a Creative Commons License