An eyegaze interface is one of the key technologies as an input device in the ubiquitous-computing society. In particular, an eyegaze communication system is very important and useful for severely handicapped users such as quadriplegic patients. Most of the conventional eyegaze tracking algorithms require specific light sources, equipment and devices. In this study, a simple eyegaze detection algorithm is proposed using a single monocular video camera. The proposed algorithm works under the condition of fixed head pose, but slight movement of the face is accepted. In our system, we assume that all users have the same eyeball size based on physiological eyeball models. However, we succeed to calibrate the physiologic movement of the eyeball center depending on the gazing direction by approximating it as a change in the eyeball radius. In the gaze detection stage, the iris is extracted from a captured face frame by using the Hough transform. Then, the eyegaze angle is derived by calculating the Euclidean distance of the iris centers between the extracted frame and a reference frame captured in the calibration process. We apply our system to an eyegaze communication interface, and verified the performance through key typing experiments with a visual keyboard on display.

Adaptation process of retina helps human visual system to see a high dynamic range scene in real world. This paper presents a simple static local adaptation method for high dynamic range image compression based on a retinal model. The proposed simple model aims at recreating the same sensations between the real scene and the range compressed image on display device when viewed after reaching steady state local adaptation respectively. Our new model takes the display adaptation into account in relation to the scene adaptation based on the retinal model. In computing local adaptation, the use of nonlinear edge preserving bilateral filter presents a better tonal rendition in preserving the local contrast and details while avoiding banding artifacts normally seen in local methods. Finally, we demonstrate the effectiveness of the proposed model by estimating the color difference between the recreated image and the target visual image obtained by trial and error method.