KL Divergence Based Person Re-identification Using Multivariate Gaussian Distributions

This paper focus on distributions of each class and proposes a novel person re-identification method using K-L divergence in the metric learning stage. The metric learning is not directly based on images or features, but directly based on distributions. The key idea of this paper is to assume that each person is a distribution and each image of a person is an instance of the distribution. Recognizing a probe becomes a task to determine which distribution the probe belongs to. In further, it assumes that the features of a person follow a multivariate Gaussian distribution and different people's distributions are different only with means of features but are same in their covariance matrices. The learning process is to find a global optimal covariance matrix among features for all of the distributions. A probe is then classified by comparing the K-L divergence with each class (distribution). The major contribution of this paper lies on the idea of distribution based metric learning methods, which is significantly different from most of the existing methods. Since the learning is among distributions, not images, the proposed model significantly reduced the computational cost and computational complexity and is much faster than traditional methods while the recognition rate is still quite competitive.