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|Title:||VIP: A unifying framework for computational eye-gaze research|
|Citation:||Ma, K.-T.,Sim, T.,Kankanhalli, M. (2013). VIP: A unifying framework for computational eye-gaze research. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8212 LNCS : 209-222. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-319-02714-2_18|
|Abstract:||Eye-gaze is an emerging modality in many research areas and applications. We present our VIP framework, which captures the dependence of eye-gaze on Visual stimulus, Intent, and Person. The unifying framework characterizes current eye-gaze computational models. It allows computer scientists to formally define their research problems and to compare with other work. We review the state-of-art computational eye-gaze research and applications with reference to our framework. With the framework, we identify gaps in eye-gaze research and present our work on the new research problem of attribute classification. The accuracy of 0.92 is achieved for classification of Introvert/Extrovert. © 2013 Springer International Publishing.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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