Semi-supervised person re-identification by similarity-embedded cycle GANs

Recently, person re-identification (PR-ID) has attracted numerous of research interest because of its broad applications. However, most of the existing PR-ID models always follow the supervised framework, which requires substantial labeled data. In fact, it is often very hard to get enough labeled training samples in many practical application scenarios. To overcome this limitation, some semi-supervised PR-ID methods have been presented more recently. Although some of these semi-supervised models achieve satisfied results, there is still much space to improve. In this paper, we propose a novel semi-supervised PR-ID by similarity-embedded cycle GANs (SECGAN). Our SECGAN model can learn cross-view features with limited labeled data by using cycle GANs. Simultaneously, to further enhance the ability of cycle GANs so that it can extract more discriminative and robust features, similarity metric subnet and specific features extracting subnet are embedded into cycle GANs. Extensive experiments have been conducted on three public PR-ID benchmark datasets, and the experimental results show that our proposed SECGAN approach outperforms several typical supervised methods and the existing state-of-the-art semi-supervised methods including traditional and deep learning semi-supervised methods.