Enhanced Eye Gaze Direction Classification Using a Combination of Face Detection, CHT and SVM

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%.