Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings
This work addresses gaze estimation for applications like human-computer interaction in real-world conditions, representing an incremental improvement by enhancing existing methods rather than introducing a new paradigm.
The paper tackles the problem of gaze estimation in unconstrained settings by introducing a learning-based method for eye region landmark localization, which enables conventional methods to compete with appearance-based approaches, achieving state-of-the-art results in iris localization and eye shape registration on real-world imagery.
Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation.