A multicue Bayesian state estimator for gaze prediction in open signed video

SJC Davies, D Agrafiotis, CN Canagarajah, DR Bull

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)
477 Downloads (Pure)


We propose a multicue gaze prediction framework for open signed video content, the benefits of which include coding gains without loss of perceived quality. We investigate which cues are relevant for gaze prediction and find that shot changes, facial orientation of the signer and face locations are the most useful. We then design a face orientation tracker based upon grid-based likelihood ratio trackers, using profile and frontal face detections. These cues are combined using a grid-based Bayesian state estimation algorithm to form a probability surface for each frame. We find that this gaze predictor outperforms a static gaze prediction and one based on face locations within the frame.
Translated title of the contributionA multicue Bayesian state estimator for gaze prediction in open signed video
Original languageEnglish
Pages (from-to)39 - 48
Number of pages10
JournalIEEE Transactions on Multimedia
Issue number1
Publication statusPublished - Jan 2009

Bibliographical note

Publisher: IEEE
Rose publication type: Journal article

Sponsorship: The work of SJC Davies was supported
by the British Broadcasting Corporation (BBC).

Terms of use: Copyright © 2009 IEEE. Reprinted from IEEE Transactions on Multimedia.

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  • eye-tracking
  • face detection
  • gaze prediction
  • video coding


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