Object Specific Deep Learning Feature and Its Application to Face Detection
This work addresses the need for efficient and robust face detection in unconstrained settings, representing an incremental improvement in feature extraction methods.
The paper tackles the problem of object-specific feature discovery in deep learning by fine-tuning a CNN to induce object-specific channels, using face detection as a case study, and results in a state-of-the-art face detector that is simple and compact.
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.