| Summary: | 碩士 === 中原大學 === 電子工程研究所 === 104 === As the popularity of smart devices gains rapidly nowadays, eye gaze detection technology can be used in various applications. For example, some smartphones can detect if the eyes of a user stay on the screen while watching a video, and then decide to pause or play the video. This technology is also applicable for analyzing the advertisement exposure, enhancing the efficiency of teaching by immediately helping children facing learning difficulties, being a communication tool between ALS (Amyotrophic Lateral Sclerosis) patients and others, and serving as a polygraph in criminal investigation.
Eye gaze detection methods fall into two major categories, contact and non-contact. The electrooculography (EOG) system and the scleral search coil method are typical examples of contact type, while the non-contact type includes a pupil position tracking method and infrared oculography. All the methods mentioned above can cause some damage to human eyes, especially the contact type. When we test a contact-type eye gaze system, the eyes of a subject must be under local anesthesia. Furthermore, most of the non-contact type approaches need to use an infrared LED source to make the pupil image more obvious, which can cause eye dryness and discomfort. Besides, the long-term use of infrared LED source would be harmful to the eyes.
In order to avoid the problems above, we consider the limbus-based approach with an ordinary light source and a common photographic lens in our experiment. However, as compared to the use of infrared LED, it is harder to highlight the eye characteristic by the ordinary light source, presenting some difficulties in subsequent experiments. Thus, we adopt the three-point circle detection method from the limbus-based approach to find the position of the pupil and incorporate an existing face detection technique so that the system can detect eyes without the use of a head supporting stand, and estimate the eye gaze point on the monitor.
In this thesis, we also focus on how to apply the system to special education, where we design a story to detect the emotion reaction of a child by employing the eye gaze system in our experiments. Through this rapid screening test for the children with autistic tendency, we may be able to reduce the burden of professionals. Since we only need to equip with camera lens and PC, a rapid screening can be carried out conveniently at home and then send the screening results to the professionals for further evaluation. We conduct the rapid screening test in a space without too much distraction for children under test, as we tell a story to attract the attention from them and interact with them. As the story goes, the subject will be asked to identify the correct emotion expression of the characters in the story. The difficulty in identifying emotion expression is exploited to detect the children with autistic tendency.
The experimental results show that the system can capture the pupils at about 94% success rate when the eyes gaze at the screen center. When the eyes gaze at left, right and up positions of a screen, the success rate is about 93%, while it is only 89% or so due to the eyelid cover when we look downward. In the gazing point estimation, location errors exist between the gazing point estimated and the exact gazing point. Although we cannot identify the exact gazing point on the screen, we are still able to distinguish the correct gazing area with large enough size on the screen. We simulate the rapid screening test by gazing at three circle objects with different sizes, where the object represents a face of the character with certain motion expression in the story. In the simulation, the two objects with the same size are separated by two different boundary lines. Experimental results show that there are little differences with different boundary lines to separate the objects. In fact, the experimental results are mainly affected by the object size. Specifically, large objects result in higher classification error, while medium and small objects have a lower chance of misclassification. Since a larger object can reflect more details of facial expression, we choose the medium size object for our rapid screening system.
In the future, we will try to improve the proposed system by reducing the estimation error of gaze point and the operation time of the system and apply the system to more areas, including education and business.
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