The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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",
}

The Robotics Institute

Menu