The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Gender and Ethnic Classification of Face Images
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Classifying Facial Attributes Using a 2-D Gabor Wavelet and Discriminant Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Genetic Feature Subset Selection for Gender Classification: A Comparison Study
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Cascaded Classification of Gender and Facial Expression using Active Appearance Models
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
ROC graphs with instance-varying costs
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Rapid and brief communication: Why direct LDA is not equivalent to LDA
Pattern Recognition
An experimental comparison of gender classification methods
Pattern Recognition Letters
Introduction to Machine Learning
Introduction to Machine Learning
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
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
Gender classification based on boosting local binary pattern
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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An automatic gender recognition algorithm based on machine learning methods is proposed. It consists of two stages: adaptive feature extraction and support vector machine classification. Both training technique of the proposed algorithm and experimental results acquired on a large image dataset are presented.