Eye tracking technology in low resolution scenarios is not a completely solved issue to date. The possibility of using eye tracking in a mobile gadget is a challenging objective that would permit to spread this technology to non-explored fields. In this paper, a knowledge based approach is presented to solve gaze estimation in low resolution settings. The understanding of the high resolution paradigm permits to propose alternative models to solve gaze estimation. In this manner, three models are presented: a geometrical model, an interpolation model and a compound model, as solutions for gaze estimation for remote low resolution systems. Since this work considers head position essential to improve gaze accuracy, a method for head pose estimation is also proposed. The methods are validated in an optimal framework, I2Head database, which combines head and gaze data. The experimental validation of the models demonstrates their sensitivity to image processing inaccuracies, critical in the case of the geometrical model. Static and extreme movement scenarios are analyzed showing the higher robustness of compound and geometrical models in the presence of user's displacement. Accuracy values of about 3° have been obtained, increasing to values close to 5° in extreme displacement settings, results fully comparable with the state-of-the-art.

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.

A multistage procedure to detect eye features is presented. Multiresolution and topographic classification are used to detect the iris center. The eye corner is calculated combining valley detection and eyelid curve extraction. The algorithm is tested in the BioID database and in a proprietary database containing more than 1200 images. The results show that the suggested algorithm is robust and accurate. Regarding the iris center our method obtains the best average behavior for the BioID database compared to other available algorithms. Additional contributions are that our algorithm functions in real time and does not require complex post processing stages.

In the past years, research in eye tracking development and applications has attracted much attention and the possibility of interacting with a computer employing just gaze information is becoming more and more feasible. Efforts in eye tracking cover a broad spectrum of fields, system mathematical modeling being an important aspect in this research. Expressions relating to several elements and variables of the gaze tracker would lead to establish geometric relations and to find out symmetrical behaviors of the human eye when looking at a screen. To this end a deep knowledge of projective geometry as well as eye physiology and kinematics are basic. This paper presents a model for a bright-pupil technique tracker fully based on realistic parameters describing the system elements. The system so modeled is superior to that obtained with generic expressions based on linear or quadratic expressions. Moreover, model symmetry knowledge leads to more effective and simpler calibration strategies, resulting in just two calibration points needed to fit the optical axis and only three points to adjust the visual axis. Reducing considerably the time spent by other systems employing more calibration points renders a more attractive model.

Calibration is one of the most tedious and often annoying aspects of many eye tracking systems. It normally consists in looking at several marks on a screen in order to collect enough data to modify the parameters of an adjustable model. Unfortunately this step is unavoidable if a competent tracking system is desired. Many efforts have been made to achieve more competent and improved eye tracking systems. Maybe the search for an accurate mathematical model is one of the least researched fields. The lack of a parametric description of the gaze estimation problem makes it difficult to find the most suitable model, and therefore generic expressions in calibration and tracking sessions are employed instead. In other words, a model based on parameters describing the elements involved in the tracking system would provide a stronger basis and robustness. The aim of this work is to build up a mathematical model totally based in realistic variables describing elements taking part in an eye tracking system employing the well known bright pupil technique i.e. user, camera, illumination and screen. The model is said to be defined when the expression relating the point the user is looking at with the extracted features of the image (glint position and center of the pupil) is found. The desired model would have to be simple, realistic, accurate and easy to calibrate.

The most significant sources of nonlinearity in the MOS resistive circuit behaviour are identified and deeply analysed in this contribution. Their influence on the MRC performance and, therefore, in the circuits containing such structure is evidenced through some examples and evaluated. Some reasonable hints about the way to minimise their undesired effects are provided.