Extracting gender discriminating features from facial needle-maps

Authors:
Jing Wu;W. A. P. Smith;E. R. Hancock;Michal Kawulok
Affiliations:
Department of Computer Science, University of York, Heslington, York, UK;Department of Computer Science, University of York, Heslington, York, UK;Department of Computer Science, University of York, Heslington, York, UK;Institute of Computer Science, Silesian University of Technology, Gliwice, Poland
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 7
Cited 0

A morphable model for the synthesis of 3D faces

Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Gender Classification of Human Faces

BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Genetic Feature Subset Selection for Gender Classification: A Comparison Study

WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Facial Shape-from-shading and Recognition Using Principal Geodesic Analysis and Robust Statistics

International Journal of Computer Vision
Weighted principal geodesic analysis for facial gender classification

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Gender classification using principal geodesic analysis and gaussian mixture models

CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Multimodal facial gender and ethnicity identification

ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we show how to extract gender discriminating features from 2.5D facial needle-maps. The standard eigenspace analysis method for non-Euclidean data is principal geodesic analysis (PGA). Based on PGA, we propose a novel supervised weighted PGA method which incorporates local weights into standard PGA to improve gender discriminating capability of the extracted features. The weight map is iteratively optimized from the labeled data, which is different from other gender relevant weights used in the literature [1]. Experimental results illustrate the effectiveness of this method and its successful application to gender classification.