Eyes are windows to our soul
Eye movements in biometrics
Eyes are one of the most complicated human organs and the analyses of eye movements may reveal a lot of information about the human being.
There are a lot of studies that analyze eye movements in order to diagnose specific diseases or to recognize state of mind .
However, surprisingly, there are only a few research trying to differentiate people on the basis of their eye movements characteristic.
The first publication about eye-movement biometric authentication was a poster presentation during 6th World Conference Biometrics'2003 in London by Pawel Kasprowski and Józef Ober .
The poster achieved the Best Poster on Technological Advance Prize what encouraged authors to further research and resulted in several publications . A
Another attempt was . Despite of quite complex experiments including text reading, moving point observation and image viewing, finally people were identified basing only on the way they look at static point (red cross) in the middle of the screen. Similarly to  the data was preprocessed using FFT and then reduced with PCA method. The authors considered four different features, from which only "velocity of gaze" was truly dynamic.
In 2006, Silver et al  proposed the first known combination of eye movements biometrics and keystroke dynamics. Data from both modalities were recorded during one experiment. However, results from keyboard dynamics were reported to give much lower error rates and only some limited properties of eye movements (like number of fixations and average fixation length) were used.
The following publication was , in which authors compared people by measuring the placement of their blind spot - what in fact used eye movements only to establish physiological properties of eyes and didn't involve usage of dynamic properties of oculomotor system.
Two interesting research papers were published during ETRA 2010 conference. Kinnunen at al.  proposed a very interesting solution based on so called 'task-independent' authentication. They didn't use any special stimulation and just recorded eye movements when a subject was watching a movie. The results obtained weren't very good (error rates about 30%) but encouraging. Another work was . The authors performed the experiment very similar to  with a jumping point stimulation. The main difference was applying so-called Oculomotor Plant Mathematical Model (OPMM) developed by authors, what improved their results.
In 2011 Deravi et al  published a paper, in which they checked performance of human identification of people looking at the static images. However, there were only three subjects in their experiment so the results cannot be considered reliable.
Since 2010 Komogortsev, after achieving a scientific grant from National Institute of Standards and Technology (NIST), was able to publish several interesting papers, in which he tried to use his OPMM in different experiments.
One of the most interesting was the text reading experiment .
Recently, he has also tried to analyze the usability of the eye movement biometrics from the prospective users point of view .
In 2012 Kasprowski and Komogorsev organized th first Eye Movement Verification and Identification Competition, what resulted in several publications . Probably the contest announced as part of one of the most important IEEE biometric conferences influenced the popularity of eye movements biometrics, because there were several new papers published in 2012 .
In the most original paper Biedert et al  analyzed eye movements of subjects during their normal activity (opening mails, reading documents). They tried to prove that it is possible to estimate whether the subject is familiar with computer desktop and that way identify the intruder (who is supposedly not familiar with it).
Another interesting publication was . Authors used face observation as visual stimulus and compared different scan paths of eye movements using original graph based method. The method was later successfully used for EMVIC datasets .
In the aforementioned eye movements publications authors retrieved different features of an eye movements signal. In most cases the signal was preprocessed to divide it into fixations (moments when the eye is looking at one place) and saccades (rapid movements from one fixation point to another) . Some authors focused on fixation information, retrieving fixation sequences  or eye micro-movements during fixations . Other authors focused on saccades calculating their velocities and accelerations . There were also approaches that used raw signals and different transformations .
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 Biedert, R., Frank, M., Martinovic, I., Song, D. Stimuli for Gaze Based Intrusion Detection. In Future Information Technology, Application, and Service (pp. 757-763). Springer Netherlands 2012.
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