A novel approach to 3-D gaze tracking using 3-D computer vision techniques is proposed in this paper. This method employs multiple cameras and multiple point light sources to estimate the optical axis of user's eye without using any userdependent parameters. Thus, it renders the inconvenient system calibration process which may produce possible calibration errors unnecessary. A real-time 3-D gaze tracking system has been developed which can provide 30 gaze measurements per second. Moreover, a simple and accurate calibration method is proposed to calibrate the gaze tracking system. Before using the system, each user only has to stare at a target point for a few (2-3) seconds so that the constant angle between the 3-D line of sight and the optical axis can be estimated. The test results of six subjects showed that the gaze tracking system is very promising achieving an average estimation error of under 1 degree.

Glint features have important roles in gaze-tracking systems. However, when the operation range of a gaze-tracking system is enlarged, the performance of glint-feature-based (GFB) approaches will be degraded mainly due to the curvature variation problem at around the edge of the cornea. Although the pupil contour feature may provide complementary information to help estimating the eye gaze, existing methods do not properly handle the cornea refraction problem, leading to inaccurate results. This paper describes a contour-feature-based (CFB) 3-D gaze-tracking method that is compatible to cornea refraction. We also show that both the GFB and CFB approaches can be formulated in a unified framework and, thus, they can be easily integrated. Furthermore, it is shown that the proposed CFB method and the GFB method should be integrated because the two methods provide complementary information that helps to leverage the strength of both features, providing robustness and flexibility to the system. Computer simulations and real experiments show the effectiveness of the proposed approach for gaze tracking.

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This work aims to develop a system for predicting age progression in children faces. Age progression prediction in children faces is critical to assist missing children searching. An integral module including feature extraction, distance measurement, and face synthesis is devised in this paper to predict faces at different ages. In the proposed method, a curvature-weighted plus bending-energy distance is employed for selecting similar facial components from an aging database. The growth curve of each facial component is used to predict the shape, size, and location of each component at a different age. Thin plate spline method is employed to synthesize a 3-D face model from the predicted components by minimizing the bending energy. Experiments are conducted to test the proposed method with various subjects and the results show that the proposed method is very promising.

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