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Conference Paper: Hidden Markov model analysis reveals better eye movement strategies in face recognition

TitleHidden Markov model analysis reveals better eye movement strategies in face recognition
Authors
KeywordsHidden Markov model
Fixation duration
Eye movement
Face recognition
Issue Date2015
Citation
The 37th Annual Conference of the Cognitive Science Society (CogSci 2015), Pasadena, CA., 22-25 July 2015. How to Cite?
AbstractHere we explored eye movement strategies that lead to better performance in face recognition with hidden Markov models (HMMs). Participants performed a standard face recognition memory task with eye movements recorded. The durations and locations of the fixations were analyzed using HMMs for both the study and the test phases. Results showed that in the study phase, the participants who looked more often at the eyes and shifted between different regions on the face with long fixation durations had better performances. The test phase analyses revealed that an efficient, short first orienting fixation followed by a more analytic pattern focusing mainly on the eyes led to better performances. These strategies could not be revealed by analysis methods that do not take individual differences in both temporal and spatial dimensions of eye movements into account, demonstrating the power of the HMM approach.
DescriptionConference Theme: Mind, Technology, and Society
Persistent Identifierhttp://hdl.handle.net/10722/212265

 

DC FieldValueLanguage
dc.contributor.authorChuk, TY-
dc.contributor.authorChan, AB-
dc.contributor.authorHsiao, J-
dc.date.accessioned2015-07-21T02:30:18Z-
dc.date.available2015-07-21T02:30:18Z-
dc.date.issued2015-
dc.identifier.citationThe 37th Annual Conference of the Cognitive Science Society (CogSci 2015), Pasadena, CA., 22-25 July 2015.-
dc.identifier.urihttp://hdl.handle.net/10722/212265-
dc.descriptionConference Theme: Mind, Technology, and Society-
dc.description.abstractHere we explored eye movement strategies that lead to better performance in face recognition with hidden Markov models (HMMs). Participants performed a standard face recognition memory task with eye movements recorded. The durations and locations of the fixations were analyzed using HMMs for both the study and the test phases. Results showed that in the study phase, the participants who looked more often at the eyes and shifted between different regions on the face with long fixation durations had better performances. The test phase analyses revealed that an efficient, short first orienting fixation followed by a more analytic pattern focusing mainly on the eyes led to better performances. These strategies could not be revealed by analysis methods that do not take individual differences in both temporal and spatial dimensions of eye movements into account, demonstrating the power of the HMM approach.-
dc.languageeng-
dc.relation.ispartofProceedings of the 37th Annual Conference of the Cognitive Science Society, CogSci 2015-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectHidden Markov model-
dc.subjectFixation duration-
dc.subjectEye movement-
dc.subjectFace recognition-
dc.titleHidden Markov model analysis reveals better eye movement strategies in face recognition-
dc.typeConference_Paper-
dc.identifier.emailHsiao, J: jhsiao@hku.hk-
dc.identifier.authorityHsiao, J=rp00632-
dc.description.naturepostprint-
dc.identifier.hkuros245544-

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