The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Genetic Feature Subset Selection for Gender Classification: A Comparison Study
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
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International Journal of Computer Vision
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm Research of Face Image Gender Classification Based on 2-D Gabor Wavelet Transform and SVM
ISCSCT '08 Proceedings of the 2008 International Symposium on Computer Science and Computational Technology - Volume 01
Facial gender classification using shape-from-shading
Image and Vision Computing
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
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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
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CGIV '11 Proceedings of the 2011 Eighth International Conference Computer Graphics, Imaging and Visualization
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ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
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RVSP '11 Proceedings of the 2011 First International Conference on Robot, Vision and Signal Processing
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This research paper introduces three robust approaches for features extraction for gender classification. The first approach is based on using Discrete Cosine Transform (DCT) and consists of two different methods for calculating features values. The second approach is based on the extraction of texture features using the gray-level cooccurrence matrix (GLCM). The third approach is based on 2D-wavelet transform. The extracted features vectors are classified using SVM. For precise evaluation, the databases used for gender evaluation are based on images from the AT@T, Faces94, UMIST, and color FERET databases. K-fold cross validation is used in training the SVM. The accuracies of gender classification when using one of the two proposed DCT methods for features extraction are 98.6聽%, 99.97聽%, 99.90聽%, and 93.3聽% with 2-fold cross validation, and 98.93聽%, 100聽%, 99.9聽%, and 92.18聽% with 5-fold cross validation. The accuracies of GLCM texture features approach for facial gender classification are 98.8聽%, 99.6聽%, 100聽%, and 93.11聽%, for AT@T, Faces94, UMIST, and FERET, databases. The accuracies for all databases when using 2D-WT are ranging between 96.18聽% and 99.6聽% except FERET and its accuracy is 92聽%.