Compensating for camera translation in video eye movement recordings by tracking a landmark selected automatically by a genetic algorithm

In the eye of the beholder: a survey of models for eyes and gaze

Despite active research and significant progress in the last 30 years, eye detection and tracking remains challenging due to the individuality of eyes, occlusion, variability in scale, location, and light conditions. Data on eye location and details of eye movements have numerous applications and are essential in face detection, biometric identification, and particular human-computer interaction tasks. This paper reviews current progress and state of the art in video-based eye detection and tracking in order to identify promising techniques as well as issues to be further addressed. We present a detailed review of recent eye models and techniques for eye detection and tracking. We also survey methods for gaze estimation and compare them based on their geometric properties and reported accuracies. This review shows that, despite their apparent simplicity, the development of a general eye detection technique involves addressing many challenges, requires further theoretical developments, and is consequently of interest to many other domains problems in computer vision and beyond.

Compensating for camera translation in video eye-movement recordings by tracking a representative landmark selected automatically by a genetic algorithm

It is common in oculomotor and vestibular research to use video or still cameras to acquire data on eye movements. Unfortunately, such data are often contaminated by unwanted motion of the face relative to the camera, especially during experiments in dynamic motion environments. We develop a method for estimating the motion of a camera relative to a highly deformable surface, specifically the movement of a camera relative to the face and eyes. A small rectangular region of interest (ROI) on the face is automatically selected and tracked throughout a set of video frames as a measure of vertical camera translation. The specific goal is to present a process based on a genetic algorithm that selects a suitable ROI for tracking: one whose translation within the camera image accurately matches the actual relative motion of the camera. We find that co-correlation, a statistic describing the time series of a large group of ROIs, predicts the accuracy of the ROIs, and can be used to select the best ROI from a group. After the genetic algorithm finds the best ROIs from a group, it uses recombination to form a new generation of ROIs that inherit properties of the ROIs from the previous generation. We show that the algorithm can select an ROI that will estimate camera translation and determine the direction that the eye is looking with an average accuracy of 0.75 degrees , even with camera translations of 2.5mm at a viewing distance of 120 mm, which would cause an error of 11 degrees without correction.