Estimating 3D Gaze Directions Using Unlabeled Eye Images via Synthetic Iris Appearance Fitting

Estimating three-dimensional (3D) human eye gaze by capturing a single eye image without active illumination is challenging. Although the elliptical iris shape provides a useful cue, existing methods face difficulties in ellipse fitting due to unreliable iris contour detection. These methods may fail frequently especially with low resolution eye images. In this paper, we propose a synthetic iris appearance fitting (SIAF) method that is model-driven to compute 3D gaze direction from iris shape. Instead of fitting an ellipse based on exactly detected iris contour, our method first synthesizes a set of physically possible iris appearances and then optimizes inside this synthetic space to find the best solution to explain the captured eye image. In this way, the solution is highly constrained and guaranteed to be physically feasible. In addition, the proposed advanced image analysis techniques also help the SIAF method be robust to the unreliable iris contour detection. Furthermore, with multiple eye images, we propose a SIAF-joint method that can further reduce the gaze error by half, and it also resolves the binary ambiguity which is inevitable in conventional methods based on simple ellipse fitting.