Gender estimation based on supervised HOG, Action Units and unsupervised CNN feature extraction
Mohammad Javidan Darugar, Loo Chu Kiong · 2017
In this paper, a deep-learning based architecture is proposed to estimate gender which includes both supervised and unsupervised facial feature extraction techniques and a deep network to fuse features and to classify them. As unsupervised techniques we have benefited deep-learning feature extraction method called convolutional neural network, and for supervised facial feature extraction we have used facial Action Units and Histogram of Oriented Gradients. Our proposed architecture has been tested on multiple datasets and for most of them the accuracy is considerably high.