A Hybrid Fuzzy Approach for Human Eye Gaze Pattern Recognition

Dingyun Zhu, Sumudu Mendis, Tom Gedeon, Arkshay Asthana, Roland Goecke

Research output: A Conference proceeding or a Chapter in BookConference contribution

10 Citations (Scopus)

Abstract

Face perception and text reading are two of the most developed visual perceptual skills in humans. Understanding which features in the respective visual patterns make them differ from each other is very important for us to investigate the correlation between human’s visual behavior and cognitive processes. We introduce our fuzzy signatures with a Levenberg-Marquardt optimization method based hybrid approach for recognizing the different eye gaze patterns when a human is viewing faces or text documents. Our experimental results show the effectiveness of using this method for the real world case. A further comparison with Support Vector Machines (SVM) also demonstrates that by defining the classification process in a similar way to SVM, our hybrid approach is able to provide a comparable performance but with a more interpretable form of the learned structure
Original languageEnglish
Title of host publication15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly
EditorsNikola Kasabov, George Coghill
Place of PublicationGermany
PublisherSpringer
Pages655-662
Number of pages8
Volume5507
ISBN (Print)9783642030390
DOIs
Publication statusPublished - 2009
Externally publishedYes
EventICONIP 2008 - Auckland, New Zealand
Duration: 25 Nov 200828 Nov 2008

Conference

ConferenceICONIP 2008
CountryNew Zealand
CityAuckland
Period25/11/0828/11/08

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  • Cite this

    Zhu, D., Mendis, S., Gedeon, T., Asthana, A., & Goecke, R. (2009). A Hybrid Fuzzy Approach for Human Eye Gaze Pattern Recognition. In N. Kasabov, & G. Coghill (Eds.), 15th International Conference on Neural Information Processing of the Asia-Pacific Neural Network Assembly (Vol. 5507, pp. 655-662). Springer. https://doi.org/10.1007/978-3-642-03040-6_80