The accuracy of eye gaze estimation using image information is affected by several factors which include image resolution, anatomical structure of the eye, and posture changes. The irregular movements of the head and eye create issues that are currently being researched to enable better use of this key technology. In this paper, we describe an effective way of estimating eye gaze from the elliptical features of one iris under the conditions of not using an auxiliary light source, a head fixing equipment, or multiple cameras. First, we provide preliminary estimation of the gaze direction, and then we obtain the vectors which describe the translation and rotation of the eyeball, by applying a central projection method on the plane which passes through the line-of-sight. This helps us avoid the complex computations involved in previous methods. We also disambiguate the solution based on experimental findings. Second, error correction is conducted on a back propagation neural network trained by a sample collection of translation and rotation vectors. Extensive experimental studies are conducted to assess the efficiency, and robustness of our method. Results reveal that our method has a better performance compared to a typical previous method.
The difference among faces under different illumination is a bottleneck in face recognition. This paper presents an
illumination compensation algorithm based on two-dimensional image information for human faces. On the assumption
that the human face shares a similar shape with spherical surface, the algorithm mainly consists of two illumination
estimation processes and one illumination compensation process. We first estimate the face information under even
illumination along the symmetrical axis of faces so as to build a standard illumination model by data fitting to the prior
statistic information. Then, we analyze the statistical distributions of the face image grayscale along the direction of
lighting. At last, using the standard model combined with linear transform and non-linear transform, we can rectify the
face image under uneven illumination to standard. The simulation results on the Yale B face database have shown that
the proposed method can realize effectively illumination compensation under wide-angle oblique lighting and very dark
lighting. In addition to simple computation, the algorithm surpasses other methods in visual effect and information
correlation.
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