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Graphical abstract Highlights • A powerful network called Asymmetric Filtering-based Dense Convolutional Neural Network (AF D-CNN) addresses person re-identification problem. • Joint Bayesian and re-ranking techniques are used to obtain the ranking lists which do not need dimensionality reduction. • Extensive experiments conducted on several datasets show that the proposed method achieves the state-of-the-art performance. Abstract Person re-identification aims at matching individuals across multiple camera views under surveillance systems. The major challenges lie in the lack of spatial and temporal cues, which makes it difficult to cope with large variations of lighting conditions, viewing angles, body poses and occlusions. How to extract multimodal features including facial features, physical features, behavioral features, color features, etc is still a fundamental problem in person re-identification. In this paper, we propose a novel Convolutional Neural Network, called Asymmetric Filtering-based Dense Convolutional Neural Network (AF D-CNN) to learn powerful features, which can extract different levels' features and take advantage of identity information. Moreover, instead of using typical metric learning methods, we obtain the ranking lists by merging Joint Bayesian and re-ranking techniques which do not need dimensionality reduction. Finally, extensive experiments show that our proposed architecture performs well on four popular benchmark datasets (CUHK01, CUHK03, Market-1501, DukeMTMC-reID). [ABSTRACT FROM AUTHOR]