Using Synthetic Images To Uncover Population Biases In Facial Landmarks Detection

TL;DR

This paper proposes using synthetic images to create comprehensive test sets for facial landmark detection, enabling better bias detection and model evaluation without relying solely on large real datasets.

Contribution

It introduces a method to generate synthetic test datasets that reveal biases in facial landmark detection models, addressing data scarcity and diversity issues.

Findings

Synthetic datasets reveal biases consistent with real data.

Synthetic test sets improve detection of model weak spots.

Method enhances bias analysis in facial landmark detection.

Abstract

In order to analyze a trained model performance and identify its weak spots, one has to set aside a portion of the data for testing. The test set has to be large enough to detect statistically significant biases with respect to all the relevant sub-groups in the target population. This requirement may be difficult to satisfy, especially in data-hungry applications. We propose to overcome this difficulty by generating synthetic test set. We use the face landmarks detection task to validate our proposal by showing that all the biases observed on real datasets are also seen on a carefully designed synthetic dataset. This shows that synthetic test sets can efficiently detect a model's weak spots and overcome limitations of real test set in terms of quantity and/or diversity.