Abstract
Recently, a novel human-machine interface, the eye-gaze input system, has been reported. This system is operated solely through the user’s eye movements. Using this system, many communication-aid systems have been developed for people suffering from severe physical disabilities, such as amyotrophic lateral sclerosis (ALS). We observed that many such people can perform only very limited head movements. Therefore, we designed an eye-gaze input system that requires no special tracing devices to track the user’s head movement. The proposed system involves the use of a personal computer (PC) and home video camera to detect the users’ eye gaze through image analysis under natural light. Eye-gaze detection methods that use natural light require only daily-life devices, such as home video cameras and PCs. However, the accuracy of these systems is frequently low, and therefore, they are capable of classifying only a few indicators. In contrast, our proposed system can detect eye gaze with high-level accuracy and confidence; that is, users can easily move the mouse cursor to their gazing point. In addition, we developed a classification method for eye blink types using the system’s feature parameters. This method allows the detection of voluntary (conscious) blinks. Thus, users can determine their input by performing voluntary blinks that represent mouse clicking. In this chapter, we present our eye-gaze and blink detection methods. We also discuss the communication-aid systems in which our proposed methods are applied.
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Abe, K., Sato, H., Matsuno, S., Ohi, S., Ohyama, M. (2016). Communication-Aid System Using Eye-Gaze and Blink Information. In: Kawulok, M., Celebi, M., Smolka, B. (eds) Advances in Face Detection and Facial Image Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-25958-1_12
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DOI: https://doi.org/10.1007/978-3-319-25958-1_12
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