# Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot

## TL;DR

This paper presents a novel feature graph fusion method combining RGB and depth data using multi-Jaccard similarity and word embeddings to improve robot recognition accuracy.

## Contribution

It introduces a new feature fusion technique leveraging extended Jaccard graphs and stochastic gradient descent for enhanced robot vision recognition.

## Key findings

- FGF is robust and effective for face and object recognition in robot applications.
- The method outperforms existing approaches on DUT RGB-D face and benchmark datasets.
- Experimental results demonstrate improved recognition accuracy and efficiency.

## Abstract

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.