Katarzyna Harezlak

Towards Accurate Eye Tracker Calibration – Methods and Procedures☆

Abstract Eye movement is a new emerging modality in human computer interfaces. With better access to devices which are able to measure eye movements (so called eye trackers) it becomes accessible even in ordinary environments. However, the first problem that must be faced when working with eye movements is a correct mapping from an output of eye tracker to a gaze point – place where the user is looking at the screen. That is why the work must always be started with calibration of the device. The paper describes the process of calibration, analyses of the possible steps and ways how to simplify this process.

Guidelines for the Eye Tracker Calibration Using Points of Regard

Eye movement data may be used for many various purposes. In most cases it is utilized to estimate a gaze point - that is a place where a person is looking at. Most devices registering eye movements, called eye trackers, return information about relative position of an eye, without information about a gaze point. To obtain this information, it is necessary to build a function that maps output from an eye tracker to horizontal and vertical coordinates of a gaze point. Usually eye movement is recorded when a user tracks a group of stimuli being a set of points displayed on a screen. The paper analyzes possible scenarios of such stimulus presentation and discuses an influence of usage of five different regression functions and two different head mounted eye trackers on the results.

The Second Eye Movements Verification and Identification Competition

The idea concerning usage of the eye movement for human identification has been known for 10 years. However, there is still lack of commonly accepted methods how to perform such identification. This paper describes the second edition of Eye Movement Verification and Identification Competition (EMVIC), which may be regarded as an attempt to provide some common basis for eye movement biometrics (EMB). The paper presents some details describing the organization of the competition, its results and formulates some conclusions for further development of EMB.

On Approaches to Discretization of Datasets Used for Evaluation of Decision Systems

The paper describes research on ways of datasets discretization, when test datasets are used for evaluation of a classifier. Three different approaches of processing for training and test datasets are presented: “independent”—where discretization is performed separately for both sets assuming that the same algorithm parameters are used; “glued”—where both sets are concatenated, discretized, and resulting set is separated to obtain training and test sets, and finally “test on learn”—where test dataset is discretized using ranges obtained from learning data. All methods have been investigated and tested in authorship attribution domain using Naive Bayes classifier.

Evaluating Quality of Dispersion Based Fixation Detection Algorithm

Information hidden in the eye movement signal can be a valuable source of knowledge about a human mind. This information is commonly used in multiple fields of interests like psychology, medicine, business, advertising, or even software developing. The proper analysis of the eye movement signal requires its elements to be extracted. The most important ones are fixations—moments when eyes are almost stable and the brain is acquiring information about the scene. There were several algorithms, aiming at detecting fixations, developed. The studies presented in this paper focused one of the most common dispersion-based algorithms—I-DT one. The various ways of evaluating its results were analyzed and compared. Some extensions in this algorithm were made as well.

Eye movement dynamics during imposed fixations

Abstract Eye movement is one of the key biological signals through which further analysis may reveal substantial information enabling greater understanding the biology of the brain and its mechanisms. Several methods for such signal processing have been developed, however new solutions are being continuously sought. This paper presents analysis of one of the main eye movement components – fixation – by usage of nonlinear time series methods. This analysis, aimed at determining the existence of chaotic behaviour, indicated by many biological systems, was based on an experiment utilising ’jumping point’ stimulus. 29 stimuli were used – presented for 3 secs in different screen positions. 24 subjects participated in the experiment consisting of two sessions conducted with a two–month interval; thus the experimental dataset included 48 recordings. The first derivative of the horizontal positions of eye movement coordinates registered during a fixation served as the time series used for reconstruction of eye movement dynamics. The analysis was performed by means of the Largest Lyapunov Exponent. Its values were studied in various time scopes of a fixation duration. A positive averaged value of this exponent, indicating chaotic behaviour, was observed for the first 200 points in the case of all studied time series. In the remining of the analysed scopes negative average exponent values were shown, however, for a number of users the eye movement signal behaviour was changing from convergent to chaotic and conversely. Additionally, the user’s eye movement dynamic was compared between sessions to check for the existence of any idiosyncratic features. The results revealed similarity existence for 30%–80% of participants depending on the analysed factor.

Implicit calibration using predicted gaze targets

The paper presents the algorithm supporting an implicit calibration of eye movement recordings. The algorithm does not require any explicit cooperation from users, yet it uses only information about a stimulus and an uncalibrated eye tracker output. On the basis of this data, probable fixation locations are calculated at first. Such a fixation set is used as an input to the genetic algorithm which task is to choose the most probable targets. Both information can serve to calibrate an eye tracker. The main advantage of the algorithm is that it is general enough to be used for almost any stimulation. It was confirmed by results obtained for a very dynamic stimulation which was a shooting game. Using the calibration function built by the algorithm it was possible to predict where a user will click with a mouse. The accuracy of the prediction was about 75%.

The Eye Tracking Methods in User Interfaces Assessment

Acquiring basic information about the user’s needs, is one of the most important problem which a user interface designer has to face. It influences the selection of the design patterns which match the user’s requirements. Most frequently lots of possible solutions could be found and the appropriate choice has to be done. The results from some previously conducted research regarding human–computer interaction proved that collecting and analysing the eye movement data may be useful in the user interfaces assessment as well. The aim of the preliminary studies presented in this paper was to analyse to what extent the eye tracing methods and eye movement metrics can support the process of user interfaces’ assessment and how this process can be automated.

Using Non-calibrated Eye Movement Data to Enhance Human Computer Interfaces

Eye movement may be regarded as a new promising modality for human computer interfaces. With the growing popularity of cheap and easy to use eye trackers, gaze data may become a popular way to enter information and to control computer interfaces. However, properly working gaze contingent interface requires intelligent methods for processing data obtained from an eye tracker. They should reflect users’ intentions regardless of a quality of the signal obtained from an eye tracker. The paper presents the results of an experiment during which algorithms processing eye movement data while 4-digits PIN was entered with eyes were checked for both calibrated and non-calibrated users.

E-Learning Database Course with Usage of Interactive Database Querying

The problem of database issues teaching with usage of Internet tools was discussed in the paper. For this purpose the database knowledge and groups of course trainees were categorized. It has influenced the construction of a database e-learning course for which the module structure has been proposed. The designed course was implemented to the MOODLE platform. Various mechanisms for database knowledge presentation and practicing were used. This mechanisms were extended of a new activity making a database interactive querying possible. The process of the MOODLE platform extending and benefits of its usage were also presented.