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 Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | The Annual Meeting of the Cognitive Science Society 2016 |
Publication status | Published - Aug 2016 |
Link(s)
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(d5c31711-6d0a-4562-9655-62bb028c3272).html |
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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.
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.
The Annual Meeting of the Cognitive Science Society 2016. 2016.
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review