1. An Eye-Tracking System based on Inner Corner-Pupil Center Vector and Deep Neural Network
- Author
-
Shu Fang Lee, Yi-Zeng Hsieh, U. Tat-Meng, Shih-Syun Lin, Zhe Fu Yeh, and Mu-Chun Su
- Subjects
Eye Movements ,BitTorrent tracker ,02 engineering and technology ,Virtual reality ,01 natural sciences ,Biochemistry ,eye tracking ,Pupil ,Article ,Analytical Chemistry ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Humans ,Computer vision ,Electrical and Electronic Engineering ,Instrumentation ,Artificial neural network ,business.industry ,010401 analytical chemistry ,deep neural network ,Gaze ,Atomic and Molecular Physics, and Optics ,0104 chemical sciences ,medicine.anatomical_structure ,inner corner-pupil center vector ,Eye tracking ,020201 artificial intelligence & image processing ,Human eye ,State (computer science) ,Artificial intelligence ,Neural Networks, Computer ,business ,Head - Abstract
The human eye is a vital sensory organ that provides us with visual information about the world around us. It can also convey such information as our emotional state to people with whom we interact. In technology, eye tracking has become a hot research topic recently, and a growing number of eye-tracking devices have been widely applied in fields such as psychology, medicine, education, and virtual reality. However, most commercially available eye trackers are prohibitively expensive and require that the user&rsquo, s head remain completely stationary in order to accurately estimate the direction of their gaze. To address these drawbacks, this paper proposes an inner corner-pupil center vector (ICPCV) eye-tracking system based on a deep neural network, which does not require that the user&rsquo, s head remain stationary or expensive hardware to operate. The performance of the proposed system is compared with those of other currently available eye-tracking estimation algorithms, and the results show that it outperforms these systems.
- Published
- 2019