Learning a Generalized Gaze Estimator from Gaze-Consistent Feature
DOI:
https://doi.org/10.1609/aaai.v37i3.25406Keywords:
CV: Biometrics, Face, Gesture & Pose, ML: Transfer, Domain Adaptation, Multi-Task Learning, CV: Adversarial Attacks & RobustnessAbstract
Gaze estimator computes the gaze direction based on face images. Most existing gaze estimation methods perform well under within-dataset settings, but can not generalize to unseen domains. In particular, the ground-truth labels in unseen domain are often unavailable. In this paper, we propose a new domain generalization method based on gaze-consistent features. Our idea is to consider the gaze-irrelevant factors as unfavorable interference and disturb the training data against them, so that the model cannot fit to these gaze-irrelevant factors, instead, only fits to the gaze-consistent features. To this end, we first disturb the training data via adversarial attack or data augmentation based on the gaze-irrelevant factors, i.e., identity, expression, illumination and tone. Then we extract the gaze-consistent features by aligning the gaze features from disturbed data with non-disturbed gaze features. Experimental results show that our proposed method achieves state-of-the-art performance on gaze domain generalization task. Furthermore, our proposed method also improves domain adaption performance on gaze estimation. Our work provides new insight on gaze domain generalization task.Downloads
Published
2023-06-26
How to Cite
Xu, M., Wang, H., & Lu, F. (2023). Learning a Generalized Gaze Estimator from Gaze-Consistent Feature. Proceedings of the AAAI Conference on Artificial Intelligence, 37(3), 3027-3035. https://doi.org/10.1609/aaai.v37i3.25406
Issue
Section
AAAI Technical Track on Computer Vision III