Robot vision is a fundamental device for human-robot interaction and robot complex tasks. In this paper, we use Kinect and propose a feature graph fusion (FGF) for robot recognition. Our feature fusion utilizes RGB and depth information to construct fused feature from Kinect. FGF involves multi-Jaccard similarity to compute a robust graph and utilize word embedding method to enhance the recognition results. We also collect DUT RGB-D face dataset and a benchmark datset to evaluate the effectiveness and efficiency of our method. The experimental results illustrate FGF is robust and effective to face and object datasets in robot applications.
@article{arxiv.1703.08378,
title = {Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot},
author = {Shenglan Liu and Muxin Sun and Wei Wang and Feilong Wang},
journal= {arXiv preprint arXiv:1703.08378},
year = {2017}
}