SalGaze: Personalizing Gaze Estimation Using Visual Saliency
This addresses the problem of user inconvenience and recalibration needs in gaze estimation systems, though it is incremental as it builds on existing CNN-based models.
The paper tackles the cumbersome need for explicit user calibration in gaze estimation by introducing SalGaze, a framework that uses visual saliency to adapt the algorithm transparently, resulting in over 24% accuracy improvement on existing methods.
Traditional gaze estimation methods typically require explicit user calibration to achieve high accuracy. This process is cumbersome and recalibration is often required when there are changes in factors such as illumination and pose. To address this challenge, we introduce SalGaze, a framework that utilizes saliency information in the visual content to transparently adapt the gaze estimation algorithm to the user without explicit user calibration. We design an algorithm to transform a saliency map into a differentiable loss map that can be used for the optimization of CNN-based models. SalGaze is also able to greatly augment standard point calibration data with implicit video saliency calibration data using a unified framework. We show accuracy improvements over 24% using our technique on existing methods.