Eye detection and tracking in image with complex background

To detect and track eye images with complex background, distinctive features of user eye are used. Generally, an eye-tracking and detection system can be divided into four steps: Face detection, eye region detection, pupil detection and eye tracking. To find the position of pupil, first, face region must be separated from the rest of the image using mixture of Gaussian, this will cause the images background to be non effective in our next steps. We used the horizontal projection obtained from face region, to separate a region containing eyes and eyebrow. This will result in decreasing the computational complexity and ignoring some factors such as bread. Finally, in proposed method points with the highest values * of are selected as the eye candidate's. The eye region is well detected among these points. Color entropy in the eye region is used to eliminate the irrelevant candidates. In the next step, we perform eye tracking. In the proposed method, eye detection and tracking are applied on testing sets, gathered from different images of face data with complex backgrounds. Experiments indicate correct detection rate of 94.9%, which is indicative of the method's superiority and high robustness.