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We present a method for discovering and exploiting object specific deeplearning features and use face detection as a case study. Motivated by theobservation that certain convolutional channels of a Convolutional NeuralNetwork (CNN) exhibit object specific responses, we seek to discover andexploit the convolutional channels of a CNN in which neurons are activated bythe presence of specific objects in the input image. A method for explicitlyfine-tuning a pre-trained CNN to induce an object specific channel (OSC) andsystematically identifying it for the human face object has been developed.Based on the basic OSC features, we introduce a multi-resolution approach toconstructing robust face heatmaps for fast face detection in unconstrainedsettings. We show that multi-resolution OSC can be used to develop state of theart face detectors which have the advantage of being simple and compact.

Object Specific Deep Learning Feature and Its Application to Face Detection