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

Stephan Brueggemann; Antoni B. Chan; Janet H. Hsiao

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

Stephan Brueggemann, Antoni B. Chan, Janet H. Hsiao

Department of Computer Science

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

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.

Original language	English
Title of host publication	The Annual Meeting of the Cognitive Science Society 2016
Publication status	Published - Aug 2016

Research Keywords

Eye-tracking
Face Recognition
Hidden Markov Model
Machine Learning

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@inproceedings{d5c317116d0a4562965562bb028c3272,

title = "Hidden Markov Modeling of eye movements with image information lead to better discovery of regions of interest",

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 alsoused 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 allowsreconstructing image information at each fixation. ",

keywords = "Eye-tracking, Face Recognition, Hidden Markov Model, Machine Learning",

author = "Stephan Brueggemann and Chan, {Antoni B.} and Hsiao, {Janet H.}",

year = "2016",

month = aug,

language = "English",

booktitle = "The Annual Meeting of the Cognitive Science Society 2016",

}

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 Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Hidden Markov Modeling of eye movements with image information lead to better discovery of regions of interest

AU - Brueggemann, Stephan

AU - Chan, Antoni B.

AU - Hsiao, Janet H.

PY - 2016/8

Y1 - 2016/8

N2 - 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 alsoused 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 allowsreconstructing image information at each fixation.

AB - 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 alsoused 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 allowsreconstructing image information at each fixation.

KW - Eye-tracking

KW - Face Recognition

KW - Hidden Markov Model

KW - Machine Learning

M3 - RGC 32 - Refereed conference paper (with host publication)

BT - The Annual Meeting of the Cognitive Science Society 2016

ER -

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

Abstract

Research Keywords

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