Estimating 3d facial shape and motion from stereo image using active appearance models with stereo constraints

Authors:
Jaewon Sung;Daijin Kim
Affiliations:
Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea;Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Korea
Venue:
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Year:
2006

Citing 5
Cited 2

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FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Using the Active Appearance Algorithm for Face and Facial Feature Tracking

RATFG-RTS '01 Proceedings of the IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS'01)
Multi-View AAM Fitting and Camera Calibration

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Real-time combined 2D+3D active appearance models

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

Adaptive active appearance model with incremental learning

Pattern Recognition Letters
A review of active appearance models

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

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Abstract

This paper proposes a new fitting algorithm which we call Stereo Active Appearance Model (STAAM). This algorithm fits a 2D+3D Active Appearance Model to stereo images acquired from calibrated vision system and computes the 3D shape and rigid motion parameters. The use of calibration information reduces the number of model parameters, restricts the degree of freedom in the model parameters, and increases the accuracy and speed of fitting. Moreover, the STAAM uses a modified inverse compositional simultaneous update fitting algorithm to reduce the fitting computation greatly. Experimental results show that (1) the modified inverse compositional simultaneous update algorithm accelerates the AAM fitting speed while keeping its fitting accuracy, (2) the STAAM improves fitting stability using calibration information.