Ollivier-Ricci Curvature For Head Pose Estimation From a Single Image

TL;DR

This paper introduces a novel head pose estimation method from a single image using Ollivier-Ricci curvature on facial landmark graphs, demonstrating competitive performance with existing approaches.

Contribution

It applies the geometric concept of Ollivier-Ricci curvature to facial landmark graphs for head pose estimation, a new approach in this domain.

Findings

Outperforms state-of-the-art methods on multiple datasets

Effective use of graph curvature for pose estimation

Combines geometric analysis with machine learning

Abstract

Head pose estimation is a crucial challenge for many real-world applications, such as attention and human behavior analysis. This paper aims to estimate head pose from a single image by applying notions of network curvature. In the real world, many complex networks have groups of nodes that are well connected to each other with significant functional roles. Similarly, the interactions of facial landmarks can be represented as complex dynamic systems modeled by weighted graphs. The functionalities of such systems are therefore intrinsically linked to the topology and geometry of the underlying graph. In this work, using the geometric notion of Ollivier-Ricci curvature (ORC) on weighted graphs as input to the XGBoost regression model, we show that the intrinsic geometric basis of ORC offers a natural approach to discovering underlying common structure within a pool of poses. Experiments on the BIWI, AFLW2000 and Pointing'04 datasets show that the ORC_XGB method performs well compared to state-of-the-art methods, both landmark-based and image-only.