Facial Asymmetry Quantification for Expression Invariant Human Identification

Yanxi Liu, Karen Schmidt, Jeffrey Cohn, and Rhiannon L. Weaver
Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition (FG'02), May, 2002, pp. 198 - 204.


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Abstract
We investigate the eff ect of quantifi ed statistical facial asymmetry as a biometric under expression variations. Our fi ndings show that the facial asymmetry measures (AsymFaces) are computationally feasible, containing discriminative information and providing synergy when combined with Fisherface and Eigen-face methods on image data of two publically available face databases (Cohn-Kanade and Feret).

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Medical Robotics Technology Center
Associated Lab(s) / Group(s): Human Identification at a Distance, Face Group, Computational Symmetry, Component Analysis, Biomedical Image Analysis
Associated Project(s): Facial Asymmetry as a Biometric and Face Recognition
Number of pages: 7

Text Reference
Yanxi Liu, Karen Schmidt, Jeffrey Cohn, and Rhiannon L. Weaver, "Facial Asymmetry Quantification for Expression Invariant Human Identification," Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition (FG'02), May, 2002, pp. 198 - 204.

BibTeX Reference
@inproceedings{Liu_2002_3919,
   author = "Yanxi Liu and Karen Schmidt and Jeffrey Cohn and Rhiannon L. Weaver",
   title = "Facial Asymmetry Quantification for Expression Invariant Human Identification",
   booktitle = "Proceedings of the 5th International Conference on Automatic Face and Gesture Recognition (FG'02)",
   pages = "198 - 204",
   month = "May",
   year = "2002",
}