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In this paper, we designed and implemented a realtime person re-identification API on a mobile robot, for a closedand open-world setting, using only the IR gray value image of a depth camera. Since common datasets are not usable we created our own dataset using the IR gray value images, the pose and image processing techniques. Then we trained the state-of-theart neural network for person re-identification with common parameters and methods. For running it in real-time, we sped up the model as well as the application. It is possible to reidentify three persons at once, at around 10 FPS. Our model reaches as closed-world setting a rank-1-accuracy of 95.5%. With an additional threshold, coming from rank-1-accuracy of closedworld setting, our real-time application reaches as open-world setting a f1-score of 79.44% and a recall of 68.44%.