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

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-

Physiopathology and recording techniques of ocular movements

En el control de la motilidad ocular intervienen varios sistemas funcionales. Los reflejos vestíbulo-oculares y optocinéticos son respuestas automáticas para compensar los movimientos de la cabeza y del entorno visual y poder estabilizar la imagen retiniana sobre un determinado punto de fijación. Los movimientos sacádicos son rápidos desplazamientos de la fijación de un punto a otro del campo visual. Los movimientos de persecución lenta consisten en el seguimiento de estímulos móviles con la mirada. Finalmente, existen movimientos involuntarios de muy escasa amplitud que se producen durante el mantenimiento de la fijación. Cada modalidad funcional de movimiento depende de circuitos neuronales específicos que trabajan coordinadamente para codificar la contracción de los músculos oculomotores correspondiente a la posición adecuada en cada momento. Estos sistemas neuronales pueden verse alterados por múltiples procesos neurológicos de diferente naturaleza y localización dando lugar a una variada gama de trastornos oculomotores. Se revisan los aspectos más destacados de la fisiopatología y de los sistemas de registro de los movimientos oculares.A number of functional systems are involved in the control of eye movements. The vestibulo-ocular and optokinetic reflexes are automatic responses that compensate for the movements of the head and those of the visual environment in order to stabilize the retinal image on a given fixation point. The saccadic movements are quick displacements of fixation from one to another point in the visual field. The smooth pursuit movements consist in the gaze following a moving target. Finally, there are some involuntary movements of very small amplitude during fixation maintenance. Each functional modality of movement depends on specific neuronal circuits that work in a coordinated manner for encoding the contraction of the oculomotor muscles to reach an adequate position at every moment. These neuronal systems call be altered by many neurological processes of different kinds and localizations, causing a broad variety of oculomotor disturbances. The most salient aspects of the physiopathology and the recording systems of eye movements are reviewed

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

Low-cost eye tracking calibration: a knowledge-based study

Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user’s features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7°, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking system