RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild

TL;DR

This paper introduces RealHePoNet, a single-stage ConvNet that accurately and efficiently estimates head pose angles in real-world images without facial landmarks, suitable for practical applications.

Contribution

The work presents a robust, fast ConvNet model trained on combined datasets for real-world head pose estimation without landmarks, achieving low error and inference time.

Findings

Average error of ~4.4° on test data

Inference time of ~6 ms per image

Effective on low-resolution grayscale images

Abstract

Human head pose estimation in images has applications in many fields such as human-computer interaction or video surveillance tasks. In this work, we address this problem, defined here as the estimation of both vertical (tilt/pitch) and horizontal (pan/yaw) angles, through the use of a single Convolutional Neural Network (ConvNet) model, trying to balance precision and inference speed in order to maximize its usability in real-world applications. Our model is trained over the combination of two datasets: 'Pointing'04' (aiming at covering a wide range of poses) and 'Annotated Facial Landmarks in the Wild' (in order to improve robustness of our model for its use on real-world images). Three different partitions of the combined dataset are defined and used for training, validation and testing purposes. As a result of this work, we have obtained a trained ConvNet model, coined RealHePoNet, that given a low-resolution grayscale input image, and without the need of using facial landmarks, is able to estimate with low error both tilt and pan angles (~4.4{\deg} average error on the test partition). Also, given its low inference time (~6 ms per head), we consider our model usable even when paired with medium-spec hardware (i.e. GTX 1060 GPU). * Code available at: https://github.com/rafabs97/headpose_final * Demo video at: https://www.youtube.com/watch?v=2UeuXh5DjAE