Work

Reducing Head Pose Estimation Data Set Bias With Synthetic Data

Year: 2025

Type: article

Abstract: Data set bias significantly impacts the performance of machine learning models, compromising their fairness, accuracy, and effectiveness. This issue is especially evident in the estimation of head pose, as current data sets suffer from a limited number of images, imbalanced data distributions, the high cost of annotation, and ethical concerns. Synthetic data offers a promising solution to address these challenges, but current semi-synthetic data sets fail to deliver satisfactory results, likely due to the limited realism of the generated faces and the heavily skewed pose distribution. In this paper, we examine the presence of data set biases in the most widely used head pose estimation benchmarks and create a data set of synthetic images using a generative model with explicit control over the head pose to mitigate this issue. Our experiments demonstrate that incorporating our synthetic images leads to improved generalization and accuracy.

Source: IEEE Access

DOI: 10.1109/access.2025.3561506

Authors: Roberto Valle, José M. Buenaposada, Luis Baumela

Institutions: Universidad Politécnica de Madrid, Universidad Rey Juan Carlos

Language: English

Topic: Face recognition and analysis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funder: Ministerio de Ciencia e Innovación