Multi-modal uniform deep learning for RGB-D person re-identification

Abstract

In this paper, we propose a multi-model uniform deep learning (MMUDL) method for RGB-D person re-identification. Unlike most existing person re-identification methods which only use RGB images, our approach recognizes people from RGB-D images so that more information such as anthropometric measures and body shapes can be exploited for re-identification. In order to exploit useful information from depth images, we use the deep network to extract efficient anthropometric features from processed depth images which also have three channels. Moreover, we design a multi-modal fusion layer to combine these features extracted from both depth images and RGB images through the network with a uniform latent variable which is robust to noise, and optimize the fusion layer with two CNN networks jointly. Experimental results on two RGB-D person re-identification datasets are presented to show the efficiency of our proposed approach.