A Mixture of Two Gender Classification Experts
Yomna Safaa El‐Din, Mohamed N. Moustafa, Hani M. K. Mahdi · 2012
This paper presents a novel method for combining the outputs of different gender classification techniques based on facial images. Merging the methods is performed by a committee machine using the Bayesian theorem. We implement and compare several well-known individual classifiers on four different datasets, then we experiment the proposed machine, and show that it significantly improves the accuracy of classification compared to individual classifiers. We also include results that address the effect of scale on the performance of classifiers.