Rendering refraction and reflection of eyeglasses for synthetic eye tracker images

Abstract

While for the evaluation of robustness of eye tracking algorithms the use of real-world data is essential, there are many applications where simulated, synthetic eye images are of advantage. They can generate labelled ground-truth data for appearance based gaze estimation algorithms or enable the development of model based gaze estimation techniques by showing the influence on gaze estimation error of different model factors that can then be simplified or extended. We extend the generation of synthetic eye images by a simulation of refraction and reflection for eyeglasses. On the one hand this allows for the testing of pupil and glint detection algorithms under different illumination and reflection conditions, on the other hand the error of gaze estimation routines can be estimated in conjunction with different eyeglasses. We show how a polynomial function fitting calibration performs equally well with and without eyeglasses, and how a geometrical eye model behaves when exposed to glasses.

GazeDirector: Fully Articulated Eye Gaze Redirection in Video

“…We do not explicitly model a full range of facial expressions such as blinking or squinting. Furthermore, we do not handle occlusions or distortion effects from eyeglasses [23]. Our model does not include the eyelashes -these are hard to model realistically, but can provide an important cue for downwards looking eye gaze.…”

Performance bottleneck analysis and resource optimized distribution method for IoT cloud rendering computing system in cyber-enabled applications

“…The client sends commands to cloud rendering servers [21][22][23][24][25][26]. The server renderings tasks according to the instructions of users, and the results will be sent back to the user to display [27][28][29][30][31]. The benefits of cloud rendering are that users do not need to worry about the hardware configuration and software compatibility of local equipment [32][33][34][35].…”

Ways of improving the precision of eye tracking data: Controlling the influence of dirt and dust on pupil detection

“…The Ellipse Selector (ElSe) Fuhl, Santini, Kübler, and Kasneci (2016) begins by applying a Canny edge filter to the eye image. Afterwards, all edges are filtered either morphologically or algorithmically.…”

“…One of the first data processing steps for video based eye-tracking is the localization of the pupil within the eyetracker image. Benchmark data for pupil detection are declared especially challenging (and in fact are) if people are simply walking around outdoors or driving a car Fuhl, Santini, Kübler, and Kasneci (2016); Fuhl, Tonsen, Bulling, and Kasneci (2016); Tonsen, Zhang, Sugano, and Bulling (2016). However, data quality means much more than the mere tracking rate Holmqvist, Nyström, and Mulvey (2012), yet it certainly is amongst the most fundamental factors.…”

“…Since it is hard to objectively quantify the amount and nature of dirt in a real experiment, we employ an image synthesis method on top of real eye-tracking videos recorded during a driving experiment. Tracking rate and performance of four state-of-the-art pupil detection algorithms, namelý Swirski and Dodgson (2014), ExCuSe Fuhl, Kübler, Sippel, Rosenstiel, and Kasneci (2015), Set Javadi, Hakimi, Barati, Walsh, and Tcheang (2015), and ElSe Fuhl, Santini, Kübler, and Kasneci (2016) are evaluated.…”