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
Boosting Sex Identification Performance
International Journal of Computer Vision
Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Framework for Multi-view Gender Classification
Neural Information Processing
On the Complementarity of Face Parts for Gender Recognition
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Fast gender recognition by using a shared-integral-image approach
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatial Gaussian mixture model for gender recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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In the paper, we propose a Bayesian classifier which exploits non-parametric model to identify the gender from the facial images. Our major contribution is that we use feature patch-based non-parametric method to generate the posteriori of male and female based on the characteristics of the labeled training image patches. Our system consists of four modules. First, we use AAM model to identify facial feature points. Facial images are represented by the overlapping feature patches around the feature points. Second, from the labeled training patches, we select a smaller subset as the patch library based on the K means clustering. Third, in training, we embed the gender characteristics of the training feature patches as the posteriori of the library patches. Fourth, in testing, we integrate the posterior of the test patches to determine the gender. The experimental results demonstrate that our proposed method is better than the conventional non-feature-patch-based methods.