Uniform and Variational Deep Learning for RGB-D Object Recognition and Person Re-Identification

In this paper, we propose a uniform and variational deep learning (UVDL) method for RGB-D object recognition and person re-identification. Unlike most existing object recognition and person re-identification methods, which usually use only the visual appearance information from RGB images, our method recognizes visual objects and persons with RGB-D images to exploit more reliable information such as geometric and anthropometric information that are robust to different viewpoints. Specifically, we extract the depth feature and the appearance feature from the depth and RGB images with two deep convolutional neural networks, respectively. In order to combine the depth feature and the appearance feature to exploit their relationship, we design a uniform and variational multi-modal auto-encoder at the top layer of our deep network to seek a uniform latent variable by projecting them into a common space, which contains the whole information of RGB-D images and has small intra-class variation and large inter-class variation, simultaneously. Finally, we optimize the auto-encoder layer and two deep convolutional neural networks jointly to minimize the discriminative loss and the reconstruction error. The experimental results on both RGB-D object recognition and RGB-D person re-identification are presented to show the efficiency of our proposed approach.