Models for gaze tracking systems

One of the most confusing aspects that one meets when introducing oneself into gaze tracking technology is the wide variety, in terms of hardware equipment, of available systems that provide solutions to the same matter, that is, determining the point the subject is looking at. The calibration process permits generally adjusting nonintrusive trackers based on quite different hardware and image features to the subject. The negative aspect of this simple procedure is that it permits the system to work properly but at the expense of a lack of control over the intrinsic behavior of the tracker. The objective of the presented article is to overcome this obstacle to explore more deeply the elements of a video-oculographic system, that is, eye, camera, lighting, and so forth, from a purely mathematical and geometrical point of view. The main contribution is to find out the minimum number of hardware elements and image features that are needed to determine the point the subject is looking at. A model has been constructed based on pupil contour and multiple lighting, and successfully tested with real subjects. On the other hand, theoretical aspects of video-oculographic systems have been thoroughly reviewed in order to build a theoretical basis for further studies

Evaluation of accurate eye corner detection methods for gaze estimation

Accurate detection of iris center and eye corners appears to be a promising approach for low cost gaze estimation. In this paper we propose novel eye inner corner detection methods. Appearance and feature based segmentation approaches are suggested. All these methods are exhaustively tested on a realistic dataset containing images of subjects gazing at different points on a screen. We have demonstrated that a method based on a neural network presents the best performance even in light changing scenarios. In addition to this method, algorithms based on AAM and Harris corner detector present better accuracies than recent high performance face points tracking methods such as Intraface

Introducing I2Head database

I2Head database has been created with the aim to become an optimal reference for low cost gaze estimation. It exhibits the following outstanding characteristics: it takes into account key aspects of low resolution eye tracking technology; it combines images of users gazing at different grids of points from alternative positions with registers of user's head position and it provides calibration information of the camera and a simple 3D head model for each user. Hardware used to build the database includes a 6D magnetic sensor and a webcam. A careful calibration method between the sensor and the camera has been developed to guarantee the accuracy of the data. Different sessions have been recorded for each user including not only static head scenarios but also controlled displacements and even free head movements. The database is an outstanding framework to test both gaze estimation algorithms and head pose estimation methods.The authors would like to acknowledge the Spanish Ministry of Economy, Industry and Competitiveness for their support under Contracts TIN2014-52897-R and TIN2017-84388-R in the framework of the National Plan of I+D+i

SeTA: semiautomatic tool for annotation of eye tracking images

Availability of large scale tagged datasets is a must in the field of deep learning applied to the eye tracking challenge. In this paper, the potential of Supervised-Descent-Method (SDM) as a semiautomatic labelling tool for eye tracking images is shown. The objective of the paper is to evidence how the human effort needed for manually labelling large eye tracking datasets can be radically reduced by the use of cascaded regressors. Different applications are provided in the fields of high and low resolution systems. An iris/pupil center labelling is shown as example for low resolution images while a pupil contour points detection is demonstrated in high resolution. In both cases manual annotation requirements are drastically reduced.Spanish Ministry of Science, Innovation and Universities, contract TIN2017-84388-

Gaze estimation problem tackled through synthetic images

In this paper, we evaluate a synthetic framework to be used in the field of gaze estimation employing deep learning techniques. The lack of sufficient annotated data could be overcome by the utilization of a synthetic evaluation framework as far as it resembles the behavior of a real scenario. In this work, we use U2Eyes synthetic environment employing I2Head datataset as real benchmark for comparison based on alternative training and testing strategies. The results obtained show comparable average behavior between both frameworks although significantly more robust and stable performance is retrieved by the synthetic images. Additionally, the potential of synthetically pretrained models in order to be applied in user's specific calibration strategies is shown with outstanding performances.Comment: https://dl.acm.org/doi/abs/10.1145/3379156.339136

Accurate pupil center detection in off-the-shelf eye tracking systems using convolutional neural networks

Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance.This research was funded by Public University of Navarra (Pre-doctoral research grant) and by the Spanish Ministry of Science and Innovation under Contract 'Challenges of Eye Tracking Off-the-Shelf (ChETOS)' with reference: PID2020-118014RB-I0