Man woman detection in surveillance images

Abstract

Human gender detection from body profile is an important task for surveillance. Most surveillance cameras are placed at a distance such that it is not possible to see people's face clearly. In this paper, we report the comparison between fast-feature pyramids and deep region-based convolutional neural network (RCNN) to detect a person in surveillance images. Since RCNN performs better in detecting a person, further training is applied to the RCNN to detect man and woman. Transfer learning strategy is used due to a small number of training images. The result shows that the trained RCNN can detect man and woman with promising result.