Eye Tracking and Head Movement Detection: A State-of-Art Survey
Eye-gaze detection and tracking have been an active research field in the past years as it adds convenience to a variety of applications. It is considered a significant untraditional method of human computer interaction. Head movement detection has also received researchers’ attention and interest as it has been found to be a simple and effective interaction method. Both technologies are considered the easiest alternative interface methods. They serve a wide range of severely disabled people who are left with minimal motor abilities. For both eye tracking and head movement detection, several different approaches have been proposed and used to implement different algorithms for these technologies. Despite the amount of research done on both technologies, researchers are still trying to find robust methods to use effectively in various applications. This paper presents a state-of-art survey for eye tracking and head movement detection methods proposed in the literature. Examples of different fields of applications for both technologies, such as human-computer interaction, driving assistance systems, and assistive technologies are also investigated.
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See complete bios of the authors in the full version of this article.
Mr. Al-Rahayfeh is pursuing a Ph.D. in computer science and engineering at the University of Bridgeport, Bridgeport, CT, USA. He received a B.S. in computer science from Mutah University and a M.S. in computer information systems from The Arab Academy for Banking and Financial Sciences.
Dr. Faezipour is an Assistant Professor in the Computer Science and Engineering and Biomedical Engineering programs, University of Bridgeport, CT, and has been the Director of the Digital/Biomedical Embedded Systems and Technology Lab since 2011. Previously, she had been a Post-Doctoral Research Associate at the University of Texas at Dallas.
This paper presents an important investigation and survey on sensor-based eye tracking methods using image analysis and pattern recognition methods. The comparative description of state-of-the-art methods in using head and eye movement models for eye-tracking would be useful for both technology and clinical research communities.
This article appeared in the 2013 issue of IEEE Journal of Translational Engineering in Health and Medicine.
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