  Gender classification aims at recognizing a person's gender. Despite the high
accuracy achieved by state-of-the-art methods for this task, there is still
room for improvement in generalized and unrestricted datasets. In this paper,
we advocate a new strategy inspired by the behavior of humans in gender
recognition. Instead of dealing with the face image as a sole feature, we rely
on the combination of isolated facial features and a holistic feature which we
call the foggy face. Then, we use these features to train deep convolutional
neural networks followed by an AdaBoost-based score fusion to infer the final
gender class. We evaluate our method on four challenging datasets to
demonstrate its efficacy in achieving better or on-par accuracy with
state-of-the-art methods. In addition, we present a new face dataset that
intensifies the challenges of occluded faces and illumination changes, which we
believe to be a much-needed resource for gender classification research.
