Hidden Markov Modeling of eye movements with image information lead to better discovery of regions of interest

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationThe Annual Meeting of the Cognitive Science Society 2016
Publication statusPublished - Aug 2016

Abstract

Hidden Markov models (HMM) can describe the spatial and temporal characteristics of eye-tracking recordings in cognitive tasks. Here, we introduce a new HMM approach. We developed HMMs based on fixation locations and we also
used image information as an input feature. We demonstrate the benefits of the newly proposed model in a face recognition study wherein an HMM was developed for every subject. Discovery of regions of interest on facial stimuli is improved as compared with earlier approaches. Moreover, clustering of the newly developed HMMs lead to very distinct groups. The newly developed approach also allows
reconstructing image information at each fixation.

Research Area(s)

  • Eye-tracking, Face Recognition, Hidden Markov Model, Machine Learning

Citation Format(s)

Hidden Markov Modeling of eye movements with image information lead to better discovery of regions of interest. / Brueggemann, Stephan; Chan, Antoni B.; Hsiao, Janet H.
The Annual Meeting of the Cognitive Science Society 2016. 2016.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review