Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition

Social behavior and many cultural etiquettes are influenced by gender. There are numerous potential applications of automatic face gender recognition such as human-computer interaction systems, content based image search, video surveillance and more. The immense increase of images that are uploaded online has fostered the construction of large labeled datasets. Recently, impressive progress has been demonstrated in the closely related task of face verification using deep convolutional neural networks. In this paper we explore the applicability of deep convolutional neural networks on gender classification by fine-tuning a pretrained neural network. In addition, we explore the performance of dropout support vector machines by training them on the deep features of the pretrained network as well as on the deep features of the fine-tuned network. We evaluate our methods on the color FERET data collection and the recently constructed Adience data collection. We report cross-validated performance rates on each dataset. We further explore generalization capabilities of our approach by conducting cross-dataset tests. It is demonstrated that our fine-tuning method exhibits state-of-the-art performance on both datasets.