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Summary

We developed a novel fully convolutional neural network (FCNN) for accurate pupil segmentation in eye tracking. This method improves gaze estimation and blink detection, even in challenging low-light conditions.

Keywords:
Convolutional neural networksDeep learningGaze estimationPupil segmentationVideo oculographyblink detection

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Area of Science:

  • Ophthalmology
  • Computer Vision
  • Biomedical Engineering

Background:

  • Accurate pupil localization is crucial for eye tracking and video-oculography (VOG).
  • Existing methods struggle with image artifacts and low-light conditions, particularly with dilated pupils.

Purpose of the Study:

  • To introduce a fully convolutional neural network (FCNN) for comprehensive pupil segmentation.
  • To integrate this FCNN into the DeepVOG system for enhanced gaze estimation.

Main Methods:

  • A novel FCNN was trained on 3946 hand-annotated VOG images.
  • The FCNN performs pupil segmentation, localization, contour estimation, and blink detection simultaneously.
  • Segmentation confidence is used to improve gaze estimation from elliptical pupil contours.

Main Results:

  • The FCNN achieves pupil center localization accuracy of approximately 1.0 pixel.
  • Gaze estimation accuracy is within 0.5 degrees.
  • The system operates at over 130 Hz with GPU acceleration and demonstrates robustness on unseen datasets.

Conclusions:

  • The FCNN-based pupil segmentation framework is accurate, robust, and generalizes well.
  • The method offers simultaneous, confident outputs for multiple eye-tracking parameters.
  • Code and models are open-sourced for public use.