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Facial Asymmetry Quantification for Expression Invariant Human Identification
Y. Liu, K. Schmidt, J. Cohn, and R.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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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).

Associated centers: VASC and MRTC
Associated labs/groups: Human Identification at a Distance, Biomedical Image Analysis, Face Group, Human Sensing, and Computational Symmetry
Associated projects: Facial Asymmetry as a Biometric and Face Recognition

Number of pages: 7

Y. Liu, K. Schmidt, J. Cohn, and R.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.

@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)",
   month = "May",
   year = "2002",
   pages = "198 - 204"
}