Gaussian Descriptor Based on Local Features for Person Re-identification

This paper proposes a novel image representation for person re-identification. Since one person is assumed to wear the same clothes in different images, the color information of person images is very important to distinguish one person from the others. Motivated by this, in this paper, we propose a simple but effective representation named Gaussian descriptor based on Local Features (GaLF). Compared with traditional color features, such as histogram, GaLF can not only represent the color information of person images, but also take the texture and spatial structure as the supplement. Specifically, there are three stages in extracting GaLF. First, pedestrian parsing and lightness constancy methods are applied to eliminate the influence of illumination and background. Then, a very simple 7-d feature is extracted on each pixel in the person image. Finally, the local features in each body part region are represented by the mean vector and covariance matrix of a Gaussian model. After getting the representation of GaLF, the similarity between two person images are measured by the distance of two set of Gaussian models based on the product of Lie group. To show the effectiveness of the proposed representation, this paper conducts experiments on two person re-identification tasks (VIPeR and i-LIDS), on which it improves the current state-of-the-art performance.