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A Real-Time Gaze Estimation Framework For Mobile Devices, Yu Feng, Nathan Goulding-Hotta, Asif Khan, Hans Reyserhove, Yuhao Zhu
A Real-Time Gaze Estimation Framework For Mobile Devices, Yu Feng, Nathan Goulding-Hotta, Asif Khan, Hans Reyserhove, Yuhao Zhu
Frameless
Tracking eyes becomes an important component to unleash new ways of human-machine interactions in augmented and virtual reality (AR/VR). To make the eye tracking system responsible, eye tracking systems need to operate at a real-time rate (> 30Hz). However, from our experiments, modern gaze tracking algorithms operate at most 5 Hz on mobile processors. In this talk, we present a real-time eye tracking algorithm that operates at 30 Hz on a mobile processor. Our algorithm achieves sub-0.5° gaze accuracy, while requiring only 30K parameters, which is one to two orders of magnitude smaller than state-of-the-art algorithms.
Deep Gaze Velocity Analysis During Mammographic Reading For Biometric Identification Of Radiologists, Hong-Jun Yoon, Folami Alamudun, Kathy Hudson, Garnetta Morin-Ducote, Georgia Tourassi
Deep Gaze Velocity Analysis During Mammographic Reading For Biometric Identification Of Radiologists, Hong-Jun Yoon, Folami Alamudun, Kathy Hudson, Garnetta Morin-Ducote, Georgia Tourassi
Journal of Human Performance in Extreme Environments
Several studies have confirmed that the gaze velocity of the human eye can be utilized as a behavioral biometric or personalized biomarker. In this study, we leverage the local feature representation capacity of convolutional neural networks (CNNs) for eye gaze velocity analysis as the basis for biometric identification of radiologists performing breast cancer screening. Using gaze data collected from 10 radiologists reading 100 mammograms of various diagnoses, we compared the performance of a CNN-based classification algorithm with two deep learning classifiers, deep neural network and deep belief network, and a previously presented hidden Markov model classifier. The study showed that …