Real-time eye feature tracking from a video image sequence using Kalman filter

Eye movement analysis is of importance in clinical studies and in research. Monitoring eye movements using video cameras has the advantage of being nonintrusive, inexpensive, and automated. The main objective of this paper is to propose an efficient approach for real-time eye feature tracking from a sequence of eye images. To this end, first we formulate a dynamic model for eye feature tracking, which relates the measurements from the eye images to the tracking parameters. In our model, the center of the iris is chosen as the tracking parameter vector and the gray level centroid of the eye is chosen as the measurement vector. In our procedure for evaluating the gray level centroid, the preprocessing step such as edge detection and curve fitting need to be performed only for the first frame of the image sequence. A discrete Kalman filter is then constructed for the recursive estimation of the eye features, while taking into account the measurement noise. Experimental results are presented to demonstrate the accuracy aspects and the real-time applicability of the proposed approach.