TL;DR
PuRe is a novel pupil detection method that significantly improves accuracy and robustness in real-world, challenging scenarios, enabling real-time pervasive eye tracking applications.
Contribution
It introduces a new edge segment selection and combination scheme with a confidence measure, enhancing pupil detection in diverse conditions.
Findings
Over 10% improvement in detection rate on challenging datasets
Enhanced precision and specificity by over 25% and 10% respectively
Operates at 120 fps for real-time applications
Abstract
Real-time, accurate, and robust pupil detection is an essential prerequisite to enable pervasive eye-tracking and its applications -- e.g., gaze-based human computer interaction, health monitoring, foveated rendering, and advanced driver assistance. However, automated pupil detection has proved to be an intricate task in real-world scenarios due to a large mixture of challenges such as quickly changing illumination and occlusions. In this paper, we introduce the Pupil Reconstructor PuRe, a method for pupil detection in pervasive scenarios based on a novel edge segment selection and conditional segment combination schemes; the method also includes a confidence measure for the detected pupil. The proposed method was evaluated on over 316,000 images acquired with four distinct head-mounted eye tracking devices. Results show a pupil detection rate improvement of over 10 percentage points…
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