Fusion of Multiple Simple Convolutional Neural Networks for Gender Classification

Abstract

Gender classification using face images is one of the most important and challenging tasks in automated face analysis, especially in unrestricted scenarios. Gender classification has become related to a growing number of applications. Nevertheless, the performance of existing methods on real-world images is still lacking. In this paper, we show that the performance of a simple convolutional neural network can be improved by learning multiple representations. We employ a simple feature fusion method using two simple convolutional neural network architectures. Our proposed method aims to replace the complex convolutional neural networks with two simple Principal Component Analysis network (PCANet) trained on different patch sizes. In addition, the high dimensional feature vector generated from each PCANet is reduced using whitening PCA. We evaluate our method on Gallagher's database which identified as among the hardest databases for gender classification. Our approach shows a comparative performance in comparison with state-of-the-art approaches.