  In this paper we propose a deep architecture for detecting people attributes
(e.g. gender, race, clothing ...) in surveillance contexts. Our proposal
explicitly deal with poor resolution and occlusion issues that often occur in
surveillance footages by enhancing the images by means of Deep Convolutional
Generative Adversarial Networks (DCGAN). Experiments show that by combining
both our Generative Reconstruction and Deep Attribute Classification Network we
can effectively extract attributes even when resolution is poor and in presence
of strong occlusions up to 80\% of the whole person figure.
