Machine vision
Real-time eye detection and tracking under various light conditions
ETRA '02 Proceedings of the 2002 symposium on Eye tracking research & applications
Evaluation of Tracking Methods for Human-Computer Interaction
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Automatic Recognition of Eye Blinking in Spontaneously Occurring Behavior
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Simultaneous eye tracking and blink detection with interactive particle filters
EURASIP Journal on Advances in Signal Processing
Statistical models of appearance for eye tracking and eye-blink detection and measurement
IEEE Transactions on Consumer Electronics
Click control: improving mouse interaction for people with motor impairments
The proceedings of the 13th international ACM SIGACCESS conference on Computers and accessibility
Toward a design of word processing environment for people with disabilities
Proceedings of the 14th international ACM SIGACCESS conference on Computers and accessibility
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
Using kernels for a video-based mouse-replacement interface
Personal and Ubiquitous Computing
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A Human-Computer Interaction (HCI) system that is designed for individuals with severe disabilities to simulate control of a traditional computer mouse is introduced. The camera-based system monitors a user's eyes and allows the user to simulate clicking the mouse using voluntary blinks and winks. For users who can control head movements and can wink with one eye while keeping their other eye visibly open, the system allows complete use of a typical mouse, including moving the pointer, left and right clicking, double clicking, and click-and-dragging. For users who cannot wink but can blink voluntarily the system allows the user to perform left clicks, the most common and useful mouse action. The system does not require any training data to distinguish open eyes versus closed eyes. Eye classification is accomplished online during real-time interactions. The system had an accuracy of 8027/8306 = 96.6% in classifying sub-images with open or closed eyes and successfully allows the users to simulate a traditional computer mouse.