HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty

TL;DR

HHP-Net is a fast, lightweight neural network that estimates head pose from keypoints, providing uncertainty measures and enabling social interaction analysis in images.

Contribution

It introduces a simple, efficient head pose estimation model that also quantifies uncertainty, enhancing analysis of social interactions.

Findings

Comparable accuracy to state-of-the-art methods

Faster inference and smaller memory footprint

Provides meaningful uncertainty estimates

Abstract

In this paper we introduce a novel method to estimate the head pose of people in single images starting from a small set of head keypoints. To this purpose, we propose a regression model that exploits keypoints computed automatically by 2D pose estimation algorithms and outputs the head pose represented by yaw, pitch, and roll. Our model is simple to implement and more efficient with respect to the state of the art -- faster in inference and smaller in terms of memory occupancy -- with comparable accuracy. Our method also provides a measure of the heteroscedastic uncertainties associated with the three angles, through an appropriately designed loss function; we show there is a correlation between error and uncertainty values, thus this extra source of information may be used in subsequent computational steps. As an example application, we address social interaction analysis in images: we propose an algorithm for a quantitative estimation of the level of interaction between people, starting from their head poses and reasoning on their mutual positions. The code is available at https://github.com/cantarinigiorgio/HHP-Net.