Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-319-02714-2_18
Title: VIP: A unifying framework for computational eye-gaze research
Authors: Ma, K.-T.
Sim, T. 
Kankanhalli, M. 
Keywords: classification
eye-gaze
framework
visual attention
Issue Date: 2013
Source: 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)
URI: http://scholarbank.nus.edu.sg/handle/10635/78423
ISBN: 9783319027135
ISSN: 03029743
DOI: 10.1007/978-3-319-02714-2_18
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

4
checked on Feb 12, 2018

Page view(s)

44
checked on Feb 16, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.