Gender classification of human faces using inference through contradictions
Xue Bai, Vladimir Cherkassky · 2008
We present an empirical study of gender classification of human faces, using new learning methodology called inference through contradictions, introduced in . This approach allows to incorporate a priori knowledge in the form of additional (unlabeled) samples, called the Universum, into the supervised learning process. Application of this methodology to gender classification shows that using this approach enables better generalization over standard SVM classification (using labeled data alone).