Gender Prediction Based on Voting of CNN Models

Abstract

Gender prediction accuracy increases as CNN architecture evolves. This paper proposes voting schemes to utilize the already developed CNN models to further improve gender prediction accuracy. Majority voting usually requires odd numbered models while proposed softmax based voting can utilize any number of models to improve accuracy. With experiments, it is shown that the voting of CNN models leads to further improvement of gender prediction accuracy and that softmax-based voters always show better gender prediction accuracy than majority voters though they consist of the same CNN models.