Person re-identification is a classical task for any multi-camera surveillance system.Most of the existing researches on re-identification are based on features extracted from RGB images.However,there are many deficiencies in RGB image processing,some of which are requiring a lot of illumination and high computation.In this paper,novel features are proposed for RGB-D person re-identification.First,the complex network approach in texture recognition is modified and its threshold function is changed for using in depth images extracted by RGB-D sensors.Then,two novel measurements named the histogram of the edge weight (HEW) and the histogram of the node strength (HNS) are introduced on complex networks.Our features fit both single-shot and multi-shot person re-identification.In the single-shot case,the HNS is extracted from only one frame while for the multi-shot case it is extracted from both one frame and multi-frames.These proposed measurements are called histogram of the spatial node strength (HSNS) and histogram of the temporal node strength (HTNS) respectively.Subsequently,these measurements are combined with skeleton features using score-level fusion.The method is evaluated using two benchmark databases and the results show that ours outperforms some state-of-the-art methods.