Although work has been conducted on smartphone gaze tracking, the existing techniques are not pervasively used because of their heavy weight and low accuracy. Our preliminary analysis shows that these techniques would work better if their models were trained with data from tablets which have large screens. In this paper, we propose GAZEL, a runtime smartphone gaze-tracking scheme that achieves high accuracy on real devices. The key idea of GAZEL, a tablet-to-smartphone transfer learning, is to train a CNN model with data collected from tablets and then transplant the model to a smartphone. To achieve the goal, we designed a new CNN-based model architecture that is head pose resilient and light enough to operate at runtime. We also exploit implicit calibration to alleviate errors caused by differences in users' visual and device characteristics. The experiment results with commercial smartphones show that GAZEL achieves 27.5% better accuracy on smartphones compared to the state-of-the-art techniques and provides gaze tracking at up to 18 fps which is practically usable at runtime.
|Title of host publication||2021 IEEE International Conference on Pervasive Computing and Communications, PerCom 2021|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2021 Mar 22|
|Event||19th IEEE International Conference on Pervasive Computing and Communications, PerCom 2021 - Virtual, Kassel, Germany|
Duration: 2021 Mar 22 → 2021 Mar 26
|Name||2021 IEEE International Conference on Pervasive Computing and Communications, PerCom 2021|
|Conference||19th IEEE International Conference on Pervasive Computing and Communications, PerCom 2021|
|Period||21/3/22 → 21/3/26|
Bibliographical noteFunding Information:
This work was supported by Next-Generation Information Computing Development Program funded by the Ministry of Science and ICT (Grant No. NRF-2017M3C4A7083677), National Research Foundation of Korea(NRF) (Grant No. NRF-2019R1A2C2004619), and Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT)(No. 2018-0-00532, Development of High-Assurance (≥ EAL6) Secure Microkernel).
© 2021 IEEE.
All Science Journal Classification (ASJC) codes
- Information Systems and Management
- Electrical and Electronic Engineering
- Management of Technology and Innovation
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Computer Vision and Pattern Recognition