Real-time 3D face tracking based on active appearance model constrained by depth data

Active Appearance Model (AAM) is an algorithm for fitting a generative model of object shape and appearance to an input image. AAM allows accurate, real-time tracking of human faces in 2D and can be extended to track faces in 3D by constraining its fitting with a linear 3D morphable model. Unfortuna...

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Bibliographic Details
Published inImage and vision computing Vol. 32; no. 11; pp. 860 - 869
Main Authors Smolyanskiy, Nikolai, Huitema, Christian, Liang, Lin, Anderson, Sean Eron
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2014
Subjects
Active Appearance Models
Face tracking
Fitting
Gradient descent
Kinect
Morphable models
Kinect
Active Appearance Models
Gradient descent
Morphable models
Face tracking
Fitting
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Summary:Active Appearance Model (AAM) is an algorithm for fitting a generative model of object shape and appearance to an input image. AAM allows accurate, real-time tracking of human faces in 2D and can be extended to track faces in 3D by constraining its fitting with a linear 3D morphable model. Unfortunately, this AAM-based 3D tracking does not provide adequate accuracy and robustness, as we show in this paper. We introduce a new constraint into AAM fitting that uses depth data from a commodity RGBD camera (Kinect). This addition significantly reduces 3D tracking errors. We also describe how to initialize the 3D morphable face model used in our tracking algorithm by computing its face shape parameters of the user from a batch of tracked frames. The described face tracking algorithm is used in Microsoft's Kinect system.
ISSN:0262-8856
1872-8138
DOI:10.1016/j.imavis.2014.08.005