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«BY GEORGIOS DIAMANTOPOULOS A THESIS SUBMITTED TO THE UNIVERSITY OF BIRMINGHAM FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF ELECTRONIC, ...»

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A tolerance angle of is used for the and classes on the bottom hemisphere (180° to 360°) to accommodate for the reduced vertical resolution that is a direct result of separating each frame into two fields. The tolerance angle is further reduced to for the bottom hemisphere of classes and (0° to 180°) to accommodate for the skew that is a direct result of the camera pointing upwards.

A calibration frame is required to initialize the eye-tracker to the subject. This is done by asking the subject to look straight ahead with his or her chin approximately parallel to the floor (posture is adjusted with the guidance of the experimenter). The eye-tracker automatically reinitializes itself to accommodate for changes in the location of the corners on every frame where the pupil position is within a contour twice as large as the initial pupil contour. In most sequences the latter contour is a little smaller than the iris. Re-initialization is not performed if the eye-tracker has re-initialized in the last 60 frames (approximately 10 seconds).

During re-initialization, the “initial pupil position” used to calculate the 2D gaze angle is

updated to from the new corner locations and such that:

An example of a visualised segment from the case study data with the speaker and subject speech, thumbnail of the selected frame and output classification (abbreviated, e.g. up-left is UL) is showing in Figure 5.

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5 Evaluation and results Given that the REACT eye-tracked is feature-based, it makes sense to evaluate its performance in extracting these features by calculating the Euclidean distance between each feature point as extracted by the eye-tracker and the feature point as manually marked by the author.

Thus, on each intermediate step of the 2D gaze calculation (detecting the pupil, calculating the iris radius and locating the corners), the appropriate set of frames were selected (the selection process will be described in detail) from the test video database and the errors were measured.

The set of manually marked frames will also be referred to as the validation data set.

It is desirable to assess the performance of each component separately and thus, for the iris radius and corners extraction algorithms that depend on previous outputs (pupil location and pupil location, iris radius respectively), they were taken from the validation data set such that there is no interference from errors from other components. The 2D gaze angle calculation algorithm was evaluated by comparing the 2D gaze angle calculated using inputs (pupil position, iris radius, corners locations) from the eye-tracker versus using inputs from the validated data set.

The pupil detection algorithm was evaluated over 12,334 frames and showed an average accuracy of 2.04 ± 3.32 pixels. Over 1856 test frames, the iris radius was on average calculated with an accuracy of 2.11 ± 1.42 pixels. Over the same test set as the iris radius, the eye corners were on average calculated to an accuracy of 8.32 ± 5.78 and 8.41 ± 5.40 pixels for the inner and outer corner respectively. The 2D gaze direction angle was on average calculated with an accuracy of 2.78 ± 1.99 degrees, a range considered practical for the target applications.

Finally, the class output by the eye-tracker was compared to a manual classification performed by the experimenter. The manual classification proved to be a much harder task than anticipated 244 as ambiguities were eminent in some cases when the eye-movement in question was on the borderline between two classes. From the total 150 eye-movements, 7 received an ambiguous classification by the experimenter and 6 were erroneously classified by the eye-tracker.

It is questionable whether ambiguous classifications can be avoided unless the subject’s eyes are also captured from another camera placed on the same level and the video may be consulted to resolve ambiguities. Of course, while this would be feasible in an experimental, for the eyetracker, setup, it would probably prove impractical for eye-tracker users conducting experiments.

All 6 classification errors were caused by the eye-movement being too close on the borderline between two classes. The classification algorithm will determine the class solely on the 2D gaze angle calculated and based on pre-set thresholds. As any other statically set threshold, it is bound to fail some of the time, when the thresholded value is very close to the threshold itself. In other words, when the gaze angle is on or close to the borderline between two classes, a human rater may be able to distinguish between the classes (though not always as proved by the 7 ambiguous ratings) but the algorithm cannot.

Example output images of the extraction of the complete set of features during calibration are shown in Figure 6.

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6 Conclusion The main objective of the current research work was to develop an eye-tracker that is able to track extreme eye-movements and calculate their gaze direction is 2D. A set of novel feature extraction algorithms were presented for extracting the location of the pupil, the iris radius and location of eye corners and calculating the gaze direction from images taken from an activelyilluminated head-mounted eye-tracker. The accuracy of the feature extraction was assessed both independently and as a whole; the eye-tracker achieved a practical level of performance that renders it acceptable for use in the target research application(s). This was further demonstrated through the pilot study that was designed and served as a case study of a real-world application.





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