Abstract
The process of face recognition is a subject of the research in this paper. 1430 recordings of participants eye movements while they were observing faces were analyzed statistically and various data mining techniques were used to extract information from eye movements signal. One of the findings is that the process of face recognition is different for different subjects and therefore formulating general rules for face recognition process may be difficult. The hypothesis was that it is possible to analyze eye movements signal to predict if the subject observing the face recognizes it. A model that automatically differentiates observations of recognized and unrecognized faces was built and the results are encouraging. One of the contributions of the paper is a conclusion that the optimal set of attributes of eye movement signal for such classification is individually specific and different for different people.
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Kasprowski, P. (2014). Mining of Eye Movement Data to Discover People Intentions. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures, and Structures. BDAS 2014. Communications in Computer and Information Science, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-06932-6_34
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DOI: https://doi.org/10.1007/978-3-319-06932-6_34
Publisher Name: Springer, Cham
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