Abstract
This paper describes a low-cost, robust, and accurate remote eye-tracking system that uses an industrial prototype smartphone with integrated infrared illumination and camera. Numerous studies have demonstrated the beneficial use of eye-tracking in domains such as neurological and neuropsychiatric testing, advertising evaluation, pilot training, and automotive safety. Remote eye-tracking on a smartphone could enable the significant growth in the deployment of applications in these domains. Our system uses a 3D gaze-estimation model that enables accurate point-of-gaze (PoG) estimation with free head and device motion. To accurately determine the input eye features (pupil center and corneal reflections), the system uses Convolutional Neural Networks (CNNs) together with a novel center-of-mass output layer. The use of CNNs improves the system’s robustness to the significant variability in the appearance of eye-images found in handheld eye trackers. The system was tested with 8 subjects with the device free to move in their hands and produced a gaze bias of 0.72°. Our hybrid approach that uses artificial illumination, a 3D gaze-estimation model, and a CNN feature extractor achieved an accuracy that is significantly (400%) better than current eye-tracking systems on smartphones that use natural illumination and machine-learning techniques to estimate the PoG.
Highlights
Closer examination of our results showed that even though the gaze-estimation method is insensitive to movements between the eye-tracker and the subject’s head, the bias in gaze estimations did change as a function of the distance between the head and the smartphone from between 0.4◦ to 2.1◦
Dramatic changes in the appearance of the face and the eyes during regular use make this task challenging. We address this issue by developing machine-learning algorithms that can estimate the position of eye features accurately for the full range of expected smartphone motions
We have presented a new hybrid eye-tracking system for smartphone
Summary
The capability to estimate where a subject is looking is known as gaze estimation or eye-tracking This technology has enhanced applications in a wide array of domains, including the measurement of advertising efficacy [1,2], instrumentation to enhance reading [3,4,5], automotive safety [6,7], pilot training [8,9,10], accessibility interfaces [11,12,13,14], and provide objective indicators of cognitive, psychiatric, and neurological states of individuals [15,16,17,18,19,20,21,22,23,24,25,26]. Due to the increasing body of research regarding what can be learned about mental states of an individual though analysis of their gaze, high-quality eye-tracking becoming a pervasive sensor in most devices (just as GPS and accelerometers have) would present another layer of privacy concerns for users of these devices [28]
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