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Object Specific Deep Learning Feature and Its Application to Face Detection

2016-09-06
Xianxu Hou, Ke Sun, Linlin Shen, Guoping Qiu

Abstract

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.

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URL

https://arxiv.org/abs/1609.01366

PDF

https://arxiv.org/pdf/1609.01366


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