Abstract
Line of sight estimation accuracy improvement is attempted using depth image (distance between user and display) and ellipsoidal model (shape of user’s eye) of cornea curvature. It is strongly required to improve line of sight estimation accuracy for perfect computer input by human eyes only. The conventional method for line of sight estimation is based on the approximation of cornea shape with ellipse function in the acquired eye image. The proposed estimation method is based on the approximation of crystalline lenses and cornea with ellipsoidal function. Therefore, much accurate approximation can be performed by the proposed method. Through experiments, it is found that depth images are useful for improvement of the line of sight estimation accuracy.
Highlights
There are some methods which allow gaze estimations and its applications for Human Computer Interaction: HCI [1]-[31]
The light source of two points is used for measurement of the cornea curvature radius of an eyeball, and two Purkinje images obtained from the cornea surface were used for it
2) NIR image and depth image is acquired with Kinect a) Iris is detected from the acquired depth image b) Distance between the iris and Kinect is estimated with the depth image c) Distance between the iris and display is estimated with the depth image 3) Cornea curvature center is estimated 4) Lune of sight is estimated Fig.[6] (a) shows an example of eye image extracted with
Summary
There are some methods which allow gaze estimations and its applications for Human Computer Interaction: HCI [1]-[31]. Paper [9] describes the method for gaze detection and line of sight estimation. An expensive stereo camera is not needed, but only a cheap simple eye camera permits a motion of a user, and the method of determining the direction of a look from a pupil center and a cornea center of curvature is proposed without the calibration which forces a user a gaze of three points. The light source of two points is used for measurement of the cornea curvature radius of an eyeball, and two Purkinje images obtained from the cornea surface were used for it. Ellipse model of cornea shape is not so appropriate for human eyes In the paper, these two problems are solved and overcome using raging image (Kinect acquires the depth between user and the display) and ellipsoidal shape model for estimation of cornea curvature.
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