International Conference on Pattern Recognition (ICPR)
Most eye tracking methods are light-based. As such they can suffer from ambient light changes when used outdoors. It has been suggested that ultrasound could provide a low power, fast, light-insensitive alternative to camera based sensors for eye tracking. We designed a bench top experimental setup to investigate the utility of ultrasound for eye tracking, and collected time of flight and amplitude data for a range of gaze angles of a model eye. We used this data as input for a machine learning model and demonstrate that we can effectively estimate gaze (gaze RMSE error of 1.021 ± 0.189 degrees with an adjusted R2 score of 89.92± 4.9).
Donglai Xiang, Timur Bagautdinov, Tuur Stuyck, Fabian Prada, Javier Romero, Weipeng Xu, Shunsuke Saito, Jingfan Guo, Breannan Smith, Takaaki Shiratori, Yaser Sheikh, Jessica Hodgins, Chenglei Wu
Ludwig Sidenmark, Mark Parent, Chi-Hao Wu, Joannes Chan, Michael Glueck, Daniel Wigdor, Tovi Grossman, Marcello Giordano
Simon Vandenhende, Dhruv Mahajan, Filip Radenovic, Deepti Ghadiyaram
