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Pingmei Xu

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    Accelerating eye movement research via accurate and affordable smartphone eye tracking
    Na Dai
    Ethan Steinberg
    Junfeng He
    Kantwon Rogers
    Venky Ramachandran
    Mina Shojaeizadeh
    Li Guo
    Nature Communications, vol. 11 (2020)
    Preview abstract Eye tracking has been widely used for decades in vision research, language and usability. However, most prior research has focused on large desktop displays using specialized eye trackers that are expensive and cannot scale. Little is known about eye movement behavior on phones, despite their pervasiveness and large amount of time spent. We leverage machine learning to demonstrate accurate smartphone-based eye tracking without any additional hardware. We show that the accuracy of our method is comparable to state-of-the-art mobile eye trackers that are 100x more expensive. Using data from over 100 opted-in users, we replicate key findings from previous eye movement research on oculomotor tasks and saliency analyses during natural image viewing. In addition, we demonstrate the utility of smartphone-based gaze for detecting reading comprehension difficulty. Our results show the potential for scaling eye movement research by orders-of-magnitude to thousands of participants (with explicit consent), enabling advances in vision research, accessibility and healthcare. View details
    Preview abstract Recent research has demonstrated the ability to estimate user’s gaze on mobile devices, by performing inference from an image captured with the phone’s front-facing camera, and without requiring specialized hardware. Gaze estimation accuracy is known to improve with additional calibration data from the user. However, most existing methods require either significant number of calibration points or computationally intensive model fine-tuning that is practically infeasible on a mobile device. In this paper, we overcome limitations of prior work by proposing a novel few-shot personalization approach for 2D gaze estimation. Compared to the best calibration-free model [11], the proposed method yields substantial improvements in gaze prediction accuracy (24%) using only 3 calibration points in contrast to previous personalized models that offer less improvement while requiring more calibration points. The proposed model requires 20x fewer FLOPS than the state-of-the-art personalized model [11] and can be run entirely on-device and in real-time, thereby unlocking a variety of important applications like accessibility, gaming and human-computer interaction. View details
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