SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning

Abstract

In this research, we present self-learn your key latent (SLYKLatent), a novel approach for enhancing gaze estimation by addressing appearance instability challenges in datasets due to aleatoric uncertainties, covariant shifts, and test domain generalization. SLYKLatent utilizes self-supervised learning for initial training with facial expression datasets, followed by refinement with a patch-based tribranch network and an inverse explained variance weighted training loss function. Our evaluation on benchmark datasets achieves a 10.98% improvement on Gaze360, supersedes the top result with 3.83% improvement on MPIIFaceGaze, and leads on a subset of ETH-XGaze by 11.59%, surpassing existing methods by significant margins. In addition, adaptability tests on RAF-DB and Affectnet show 86.4% and 60.9% accuracies, respectively. Ablation studies confirm the effectiveness of SLYKLatent's novel components. This approach has strong potential in human–robot interaction.