I. Introduction
Person re-identification (re-ID) [1]–[4] has attracted considerable academic attention, attributable to its tremendous capability in security applications, such as person tracking [5], behavior analysis [6] and so on. The scope of person re-ID is to match the images of the same target person captured by non-overlapping cameras [7]. Although deep convolutional neural networks [8]–[10] have significantly prompted the development of person re-ID, the vast majority of existing methods typically require large-scale labelled training data that entails high annotation cost, restricting the practicability and expansibility of person re-ID in real-world applications. Nowadays, how to perform person re-ID in an unsupervised manner is becoming a new research hotspot.