Person re-identification by kernel null space marginal Fisher analysis

Abstract Distance metric learning has been widely applied for person re-identification. However, the typical Small Sample Size (SSS) problem, which is induced by high dimensional feature and limited training samples in most re-identification datasets, may lead to a sub-optimal metric. In this work, we propose to embed samples into a discriminative null space based on Marginal Fisher Analysis (MFA) to overcome the SSS problem. In such a null space, multiple images of the same pedestrian are collapsed to a single point, resulting the extreme Marginal Fisher Criterion. We theoretically analyze the null space and derive its closed-form solution which can be computed very efficiently. To deal with the heavy storage burden in computation, we further extend the proposed method to kernel version, which is called Kernel Null Space Marginal Fisher Analysis (KNSMFA). Experiments on four challenging person re-identification datasets show that KNSMFA uniformly outperforms state-of-the-art approaches.