3D Face Geometry Recovery Via Group-wise Optical Flow

Hui Fang, Nick Costen, David Cristinacce, John Darby

Research output: Contribution to conferencePoster

5 Citations (Scopus)

Abstract

We describe an algorithm for automatically finding correspondences from face video sequences. This method is useful to many applications such as face tracking, face modeling and 3D face recovery. Given a sequence of images, the face feature points are tracked by a model-constraint optical flow algorithm. By employing a Minimum Description Length (MDL) point-refinement framework, the drift-off error caused by the optical flow algorithm can be reduced and the correspondences can be matched robustly by optimizing the statistical model. As a result, the face is able to be tracked precisely. Furthermore, it offers a new method of building an appearance model automatically. The objective root mean square error (RMSE) is used to prove the efficiency of the algorithm. At the same time, the performance is evaluated subjectively by generating 3D face models based upon it.
Original languageEnglish
Pages1-8
DOIs
Publication statusPublished - 2008
EventIEEE International Conference on Facial and gesture recognition - Amsterdam, Netherlands
Duration: 1 Jan 2008 → …

Conference

ConferenceIEEE International Conference on Facial and gesture recognition
CountryNetherlands
CityAmsterdam
Period1/01/08 → …

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Fang, H., Costen, N., Cristinacce, D., & Darby, J. (2008). 3D Face Geometry Recovery Via Group-wise Optical Flow. 1-8. Poster session presented at IEEE International Conference on Facial and gesture recognition, Amsterdam, Netherlands. https://doi.org/10.1109/AFGR.2008.4813303