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Conference Paper: Understanding eye movements in face recognition with hidden Markov model
Title | Understanding eye movements in face recognition with hidden Markov model |
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Authors | |
Issue Date | 2013 |
Publisher | Cognitive Science Society. |
Citation | The 35th Annual Conference of the Cognitive Science Society (CogSci 2013), Berlin, Germany, 31 July-3 August, 2013. In CogSci 2013 Proceedings, 2013, p. 328-333 How to Cite? |
Abstract | In this paper we propose a hidden Markov model (HMM)-based method to analyze eye movement data. We conducted a simple face recognition task and recorded eye movements and performance of the participants. We used a variational Bayesian framework for Gaussian mixture models to estimate the distribution of fixation locations and modelled the fixation and transition data using HMMs. We showed that using HMMs, we can describe individuals’ eye movement strategies with both fixation locations and transition probabilities. By clustering these HMMs, we found that the strategies can be categorized into two subgroups; one was more holistic and the other was more analytical. Furthermore, we found that correct and wrong recognitions were associated with distinctive eye movement strategies. The difference between these strategies lied in their transition probabilities. |
Description | Fulltext in: http://mindmodeling.org/cogsci2013/papers/0085/paper0085.pdf |
Persistent Identifier | http://hdl.handle.net/10722/187076 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Chuk, TY | en_US |
dc.contributor.author | Ng, A | en_US |
dc.contributor.author | Coviello, E | en_US |
dc.contributor.author | Chan, AB | en_US |
dc.contributor.author | Hsiao, JHW | en_US |
dc.date.accessioned | 2013-08-20T12:28:46Z | - |
dc.date.available | 2013-08-20T12:28:46Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | The 35th Annual Conference of the Cognitive Science Society (CogSci 2013), Berlin, Germany, 31 July-3 August, 2013. In CogSci 2013 Proceedings, 2013, p. 328-333 | en_US |
dc.identifier.isbn | 9780976831891 | - |
dc.identifier.uri | http://hdl.handle.net/10722/187076 | - |
dc.description | Fulltext in: http://mindmodeling.org/cogsci2013/papers/0085/paper0085.pdf | - |
dc.description.abstract | In this paper we propose a hidden Markov model (HMM)-based method to analyze eye movement data. We conducted a simple face recognition task and recorded eye movements and performance of the participants. We used a variational Bayesian framework for Gaussian mixture models to estimate the distribution of fixation locations and modelled the fixation and transition data using HMMs. We showed that using HMMs, we can describe individuals’ eye movement strategies with both fixation locations and transition probabilities. By clustering these HMMs, we found that the strategies can be categorized into two subgroups; one was more holistic and the other was more analytical. Furthermore, we found that correct and wrong recognitions were associated with distinctive eye movement strategies. The difference between these strategies lied in their transition probabilities. | - |
dc.language | eng | en_US |
dc.publisher | Cognitive Science Society. | en_US |
dc.relation.ispartof | Proceedings of the 35th Annual Conference of the Cognitive Science Society, CogSci 2013 | en_US |
dc.title | Understanding eye movements in face recognition with hidden Markov model | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Hsiao, JHW: jhsiao@hku.hk | en_US |
dc.identifier.authority | Hsiao, JHW=rp00632 | en_US |
dc.identifier.hkuros | 220290 | en_US |
dc.identifier.spage | 328 | en_US |
dc.identifier.epage | 333 | en_US |
dc.publisher.place | Austin, Texas, USA | - |