Enhanced Eye Gaze Direction Classification Using a Combination of Face Detection, CHT and SVM
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Authors
Al-Rahayfeh, Amer A.
Faezipour, Miad
Issue Date
2014-03-28
Type
Presentation
Language
en_US
Keywords
Faculty research day , Engineering , Eye gaze detection
Alternative Title
Abstract
Automatic estimation of eye gaze direction is an interesting research area in the field of computer vision that is growing rapidly with its wide range of potential applications. However, it is still a very challenging task to implement a robust eye gaze classification system. This paper proposes a robust eye detection system that uses face detection for finding the eyes region. The Circular Hough Transform (CHT) is used for locating the center of the iris. The parameters of the Circular Hough Transform are dynamically calculated based on the detected face information. A new method for eye gaze direction classification using Support Vector Machine (SVM) is introduced and combined with Circular Hough Transform to complete the task required. The experiments were performed on a database containing 4000 images of 40 subjects from different ages and genders. The algorithm achieved a classification accuracy of up to 92.1%.