title: Understanding eye movements in face recognition using hidden Markov models.
abstract: We use a hidden Markov model (HMM) based approach to analyze eye movement data in face recognition. HMMs are statistical models that are specialized in handling time-series data. We conducted a face recognition task with Asian participants, and model each participant's eye movement pattern with an HMM, which summarized the participant's scan paths in face recognition with both regions of interest and the transition probabilities among them. By clustering these HMMs, we showed that participants' eye movements could be categorized into holistic or analytic patterns, demonstrating significant individual differences even within the same culture. Participants with the analytic pattern had longer response times, but did not differ significantly in recognition accuracy from those with the holistic pattern. We also found that correct and wrong recognitions were associated with distinctive eye movement patterns; the difference between the two patterns lies in the transitions rather than locations of the fixations alone.
authors.affiliation: Department of Psychology, University of Hong Kong, Hong Kong.
authors.forename: Tim
authors.initials: T
authors.lastname: Chuk
authors.affiliation: Department of Computer Science, City University of Hong Kong, Hong Kong.
authors.forename: Antoni B
authors.initials: AB
authors.lastname: Chan
authors.affiliation: Department of Psychology, University of Hong Kong, Hong Kong.
authors.forename: Janet H
authors.initials: JH
authors.lastname: Hsiao
mesh_terms: D000293:Adolescent; D005133:Eye Movements / Q000502:physiology*; D005145:Face / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D015233:Models, Statistical; D010364:Pattern Recognition, Visual / Q000502:physiology*; D011336:Probability; D021641:Recognition, Psychology / Q000502:physiology*; D055815:Young Adult
keywords: eye movement; face recognition; hidden Markov models