EMPATH: face, emotion, and gender recognition using holons
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Gender Classification with Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Integrating Independent Components and Support Vector Machines for Gender Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A Method of Gender Classification by Integrating Facial, Hairstyle, and Clothing Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Gender recognition: A multiscale decision fusion approach
Pattern Recognition Letters
Gender Classification Using Local Directional Pattern (LDP)
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Learning local binary patterns for gender classification on real-world face images
Pattern Recognition Letters
Gender recognition via locality preserving tensor analysis on face images
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
The Nonsubsampled Contourlet Transform: Theory, Design, and Applications
IEEE Transactions on Image Processing
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This paper presents a gender recognition method by combining three types of effective features, including facial texture features, hair geometry features, and mustache features. The recognition method includes two phases which are based on the AdaBoost algorithm. In the first phase, facial and hair features are extracted from a face image and then fed into a classifier to roughly classify the image into male and female classes. In the second phase, the mustache features are added into the feature vector of the female patterns which classified into female class in the first phase. The female patterns are then classified again to correct the misclassified patterns. The FERET database is used to evaluate our method in the experiment. In the FERET data set, 659 images are chosen in which 366 of them are used as training data and the rest are regarded as test data. The best classification rate of the proposed method achieves 96.25%.