Person re-identification by kernel null space marginal Fisher analysis.

•We propose to learn a discriminative null space for person re-identification.•The proposed method can effectively overcome the small sample size problem in distance metric learning.•A kernel version is further developed to deal with highly nonlinear appearance patterns.•Remarkable performance is achieved on four challenging datasets (VIPeR, GRID, PRID450S, and 3DPeS).