Abstract
This paper presents a method to detect the eye contour using recurrent neural network. Log-Polar transform is used to convert the changes of eye rotation and scales into translations. Principal component analysis is applied on the eye RGB color channels in order to obtain a stable texture representation regardless lighting conditions. A recurrent neural network is trained to discern the patterns of the texture values around the eye contour as well as to model the eye contour appearance with different eye states. The experimental results have verified that the recurrent network is able to detect the eye contour robustly. The proposed method outperforms the conventional methods in terms of accuracy and detection time.
Highlights
Human-Machine Interface (HCI) systems have become one of the most attractive fields in image processing [1] [2]
4.1 Setup The proposed method, Active shape model (ASM) and Deformable Template (DT) have been implemented in the environment of visual studio C++ 2008
The second database is locally collected to permit a considerable change of eye rotation with different scales and lighting conditions
Summary
Human-Machine Interface (HCI) systems have become one of the most attractive fields in image processing [1] [2]. It has been proven that using eyes in interface systems is faster than hands [3]. Eye features such as eye corners, gaze point, pupil location, eye contour and blinking frequency can be employed in many applications. Car companies try to increase the human safety by tracking the blinking frequency in order to detect the driver fatigue [4]. The handicapped people who have problems in controlling wheel chair by hands can be helped using eye gaze estimation techniques [5]
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