  We present a method for discovering and exploiting object specific deep
learning features and use face detection as a case study. Motivated by the
observation that certain convolutional channels of a Convolutional Neural
Network (CNN) exhibit object specific responses, we seek to discover and
exploit the convolutional channels of a CNN in which neurons are activated by
the presence of specific objects in the input image. A method for explicitly
fine-tuning a pre-trained CNN to induce an object specific channel (OSC) and
systematically identifying it for the human face object has been developed.
Based on the basic OSC features, we introduce a multi-resolution approach to
constructing robust face heatmaps for fast face detection in unconstrained
settings. We show that multi-resolution OSC can be used to develop state of the
art face detectors which have the advantage of being simple and compact.
