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 crossvalidated
performance rates on each dataset. We further explore
generalization capabilities of our approach by conducting crossdataset
tests. It is demonstrated that our fine-tuning method
exhibits state-of-the-art performance on both datasets.