Tracking facial features using probabilistic network

Hai Tao, Ricardo Lopez, Thomas S Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, an improved model-based automatic face/head tracking algorithm is presented. The input to the system is a video sequence including a head-and-shoulders scene. The outputs are the detected global head movements and the local facial feature motions. To estimate the global head position, the 2D image coordinates of feature points are mapped to 3D by assuming the projection is approximately scaled orthographic. After this initial estimation, Kalman filter is employed to improve the temporal stability. For non-rigid local facial motion tracking, a probabilistic network is constructed to encode the information about the relative positions and velocities among various facial feature points. This network is trained in a supervised fashion and is applied later as structural constraints to incorporate with the traditional template matching method. Currently, the conditional distributions employed in the network are two-dimensional. They are obtained bp learning front front-view sequences. To apply this network to 3D face/head tracking, pose compensation must be performed based on the estimated head poses.

Original languageEnglish (US)
Title of host publicationProceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998
PublisherIEEE Computer Society
Pages166-170
Number of pages5
ISBN (Print)0818683449, 9780818683442
DOIs
StatePublished - Jan 1 1998
Event3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998 - Nara, Japan
Duration: Apr 14 1998Apr 16 1998

Publication series

NameProceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998

Other

Other3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998
CountryJapan
CityNara
Period4/14/984/16/98

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Tao, H., Lopez, R., & Huang, T. S. (1998). Tracking facial features using probabilistic network. In Proceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998 (pp. 166-170). [670943] (Proceedings - 3rd IEEE International Conference on Automatic Face and Gesture Recognition, FG 1998). IEEE Computer Society. https://doi.org/10.1109/AFGR.1998.670943