In the cases of paralysis so severe that a person's ability to control movement is limited to the muscles around the eyes, eye movements or blinks are the only way for the person to communicate. Interfaces that assist in such communication are often intrusive, require special hardware, or rely on active infrared illumination. A nonintrusive communication interface system called EyeKeys was therefore developed, which runs on a consumer-grade computer with video input from an inexpensive Universal Serial Bus camera and works without special lighting. The system detects and tracks the person's face using multiscale template correlation. The symmetry between left and right eyes is exploited to detect if the person is looking at the camera or to the left or right side. The detected eye direction can then be used to control applications such as spelling programs or games. The game ldquoBlockEscaperdquo was developed to evaluate the performance of EyeKeys and compare it to a mouse substitution interface. Experiments with EyeKeys have shown that it is an easily used computer input and control device for able-bodied people and has the potential to become a practical tool for people with severe paralysis.

Tracking methods are evaluated in a real-time feature tracking system used for human-computer interaction (HCI). The Camera Mouse, a HCI system for people with severe disabilities that interprets video input to manipulate the mouse pointer, was improved and used as the test platform for this study. Tracking methods tested are the Lucas-Kanade tracker and a tracker based on normalized correlation. Both methods are evaluated with and without multidimensional Kalman filters. Two-, four-, and six-dimensional filters are tested to model feature location, velocity, and acceleration. The various tracker and filter combinations are evaluated for accuracy, computational efficiency, and practicality. The normalized correlation coefficient tracker without Kalman filtering is found to be the tracker best suited for a variety of HCI tasks.

A method for a real-time vision system that automatically detects a user's eye blinks and accurately measures their durations is introduced. The system is intended to provide an alternate input modality to allow people with severe disabilities to access a computer. Voluntary long blinks trigger mouse clicks, while involuntary short blinks are ignored. The system enables communication using "blink patterns:" sequences of long and short blinks which are interpreted as semiotic messages. The location of the eyes is determined automatically through the motion of the user's initial blinks. Subsequently, the eye is tracked by correlation across time, and appearance changes are automatically analyzed in order to classify the eye as either open or closed at each frame. No manual initialization, special lighting, or prior face detection is required. The system has been tested with interactive games and a spelling program. Results demonstrate overall detection accuracy of 95.61% and an average rate of 28 frames per second.

No abstract available.