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Gender Classification From Face Images Using Mutual Information and Feature Fusion.
- Source :
-
International Journal of Optomechatronics . 2012, Vol. 6 Issue 1, p92-119. 28p. - Publication Year :
- 2012
-
Abstract
- In this article we report a new method for gender classification from frontal face images using feature selection based on mutual information and fusion of features extracted from intensity, shape, texture, and from three different spatial scales. We compare the results of three different mutual information measures: minimum redundancy and maximal relevance (mRMR), normalized mutual information feature selection (NMIFS), and conditional mutual information feature selection (CMIFS). We also show that by fusing features extracted from six different methods we significantly improve the gender classification results relative to those previously published, yielding 99.13% of the gender classification rate on the FERET database. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15599612
- Volume :
- 6
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- International Journal of Optomechatronics
- Publication Type :
- Academic Journal
- Accession number :
- 73254257
- Full Text :
- https://doi.org/10.1080/15599612.2012.663463