Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Abstract

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.

References in corpus (9)

• Adam: A Method for Stochastic Optimization
• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• DeepPose: Human Pose Estimation via Deep Neural Networks
• Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
• Appearance-Based Gaze Estimation in the Wild
• It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation
• TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking
• Eye Tracking for Everyone
• Robust Real-Time Multi-View Eye Tracking

Cited by in corpus (22)

• Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications
• Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework
• Sketch-based Creativity Support Tools using Deep Learning
• Visual Intelligence through Human Interaction
• The Story in Your Eyes: An Individual-difference-aware Model for Cross-person Gaze Estimation
• Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework
• Enjoy the Ride Consciously with CAWA: Context-Aware Advisory Warnings for Automated Driving
• Automatic Gaze Analysis: A Survey of Deep Learning based Approaches
• RIT-Eyes: Rendering of near-eye images for eye-tracking applications
• Human-Object Interaction Detection:A Quick Survey and Examination of Methods
• EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking
• Privacy-Preserving Eye Videos using Rubber Sheet Model
• Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning
• LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images
• Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild
• SalGaze: Personalizing Gaze Estimation Using Visual Saliency
• $p{i}_t$- Enhancing the Precision of Eye Tracking using Iris Feature Motion Vectors
• A Novel Binocular Eye-Tracking SystemWith Stereo Stimuli for 3D Gaze Estimation
• Mapping the ocular surface from monocular videos with an application to dry eye disease grading
• HybridGazeNet: Geometric model guided Convolutional Neural Networks for gaze estimation
• Non-contact Real time Eye Gaze Mapping System Based on Deep Convolutional Neural Network
• Bayesian Eye Tracking