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

• URL: http://arxiv.org/abs/2402.01555v2
• Date: Wed, 13 Nov 2024 11:44:10 GMT
• Title: SLYKLatent: A Learning Framework for Gaze Estimation Using Deep Facial Feature Learning
• Authors: Samuel Adebayo, Joost C. Dessing, Seán McLoone,
• Abstract summary: We present SLYKLatent, a novel approach for enhancing gaze estimation by addressing appearance instability challenges in datasets. SLYKLatent utilizes Self-Supervised Learning for initial training with facial expression datasets, followed by refinement with a patch-based tri-branch network. Our evaluation on benchmark datasets achieves a 10.9% improvement on Gaze360, supersedes top MPIIFaceGaze results with 3.8%, and leads on a subset of ETH-XGaze by 11.6%.
• Score: 0.0
• License:
• Abstract: In this research, we present 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 tri-branch network and an inverse explained variance-weighted training loss function. Our evaluation on benchmark datasets achieves a 10.9% improvement on Gaze360, supersedes top MPIIFaceGaze results with 3.8%, and leads on a subset of ETH-XGaze by 11.6%, surpassing existing methods by significant margins. 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.

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