EMPATH: face, emotion, and gender recognition using holons
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
SexNet: A neural network identifies sex from human faces
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Learning Gender with Support Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Gender and Ethnic Classification of Face Images
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Genetic Feature Subset Selection for Gender Classification: A Comparison Study
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Boosting Sex Identification Performance
International Journal of Computer Vision
Facial Shape-from-shading and Recognition Using Principal Geodesic Analysis and Robust Statistics
International Journal of Computer Vision
Learning Mixture Models for Gender Classification Based on Facial Surface Normals
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Integrating independent components and linear discriminant analysis for gender classification
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Improving gender recognition using genetic algorithms
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
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We apply a semi-supervised learning method to perform gender determination. The aim is to select the most discriminating feature components from the eigen-feature representation of faces. By making use of the information provided by both labeled and unlabeled data, we successfully reduce the size of the labeled data set required for gender feature selection, and improve the classification accuracy. Instead of using 2D brightness images, we use 2.5D facial needle-maps which reveal more directly facial shape information. Principal geodesic analysis (PGA), which is a generalization of principal component analysis (PCA) from data residing in a Euclidean space to data residing on a manifold, is used to obtain the eigen-feature representation of the facial needle-maps. In our experiments, we achieve 90.50% classification accuracy when 50% of the data are labeled. This performance demonstrates the effectiveness of this method for gender classification using a small labeled set, and the feasibility of gender classification using the facial shape information.