A Method to Estimate Eye Gaze Direction When Wearing Glasses Eye gaze input system has been used for severe physical disabilities such as ALS patients who are difficult to communicate with others. In conventional eye gaze input systems under natural light, the condition that human wears glasses has not been into account. In this study, we propose a method to estimate eye gaze direction of human who wears glasses. In the method, image processing is conducted as follows; the parts of glasses in eye image after thresholding are identified by conducting labeling processing, and removed them to detect inner corner of the eye, outer corner of the eye, and part of black eye. A three layer neural network is composed to estimate eye gaze direction. In the neural network, the input signals are position coordinates of inner corner of the eye, outer corner of the eye, and center of black eye, and the output signals are the eye gaze directions. To examine the effectiveness of the proposed method, experiments in which human subjects follow a moving target on the screen by eye movement are conducted for both conditions of wearing glasses and without glasses. As the results, it is confirmed that the accuracy of the proposed method for the condition wearing glasses is as good as that without glasses, which shows the effectiveness of the proposed method.

Predicting Eye Fixations Using Computer Vision Techniques The goal of this work is to study mechanisms of visual attention to assist visual perception for patients suffering from agerelated macular degeneration (AMD) or retinitis pigmentosa (RP) through artificial retina devices. We present a method to predict where humans look; we extend a visual saliency model by incorporating additional features and use this model to obtain saliency maps. These are thresholded at different scales to estimate the points of an image upon which the human eye fixates as well as the exact sequence of these fixations. The sequence of fixations extracted is further used to identify the part of the image that will mostly attract visual attention. Contrary to most existing approaches our method can indicate specific coordinates for the fixation points rather than generic areas that may attract visual attention and is thus more appropriate to imitate human fixations. Our method performs marginally better than the wellknown method for saliency prediction we compare against (xe2x89x8876% accuracy) and very satisfactorily in terms of estimating the sequence of fixations upon any given image (up to 98% accuracy).