Kernelized random KISS metric learning for person re-identification

Person re-identification is critical for human tracking in the video surveillance which has attracted more and more attention in recent years. Various recent approaches have made great progress in re-identification performance using metric learning techniques and among them, Keep It Simple and Straightforward (KISS) metric learning method has shown remarkably importance because of its simpleness and high-efficiency. The KISS method is based on an assumption that the differences between feature pairs obey the Gaussian distribution. However, for most existing features of person re-identification, the distributions of differences between feature pairs are irregular and undulant. Therefore, prior to the Guassian based metric learning step, it's important to augment the Guassian distribution of data without losing discernment. Moreover, most metric learning methods were greatly influenced by the small sample size (SSS) problem and the KISS method is no exception, which causing the inexistence of inverse of covariance matrices. To solve the above two problems, we present Kernelized Random KISS (KRKISS) metric learning method. By transforming the original features into kernelized features, the differences between feature pairs can better fit the Gaussian distribution and thus they can be more suitable for the Guassian assumption based models. To solve the inverse of covariance matrix estimation problem, we apply a random subspace ensemble method to obtain exact estimation of covariance matrix by randomly selecting and combining several different subspaces. In each subspace, the influence of SSS problem can be minimized. Experimental results on three challenging person re-identification datasets demonstrate that the KRKISS method significantly improves the KISS method and achieves better performance than most existing metric learning approaches.