Clustering using a random walk on graph for head pose estimation

Abstract

In this paper, the problem of head pose estimation is described. The solution consists of several stages. The clustering is a critical step. The clustering of feature points of the image is consuming and important step that needs to simplify and speed up. For this task, it is proposed to use the properties of a random walk on the graph. The random walk can lead to a measure of cluster cohesion. This approach is closely related to spectral graph theory. The paper presents formulas, steps of the algorithm and an example of calculations. Experiments and comparisons are made with the closest analogue, the method of normalized cut.