Human Identification versus Expression Classification via Bagging on Facial Asymmetry

Yanxi Liu and S. Mitra
tech. report CMU-RI-TR-03-08, Robotics Institute, Carnegie Mellon University, May, 2003


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Abstract
We demonstrate a dual usage of quantified facial asymmetry for (1) human identification under expression variations and (2) expression classification across different human subjects. Our experiments show the effectiveness of using statistical bagging and feature subspace selection BEFORE applying classiffers such as Linear Discriminant Analysis. This preprocessing allows the same type but different dimensions of image features to be discriminative for two seemingly conflicting classiffcation goals. Statistically significant improvements are found when facial asymmetry features are combined into classical classifiers.

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, Computational Symmetry, Biomedical Image Analysis
Associated Project(s): Facial Asymmetry as a Biometric

Text Reference
Yanxi Liu and S. Mitra, "Human Identification versus Expression Classification via Bagging on Facial Asymmetry," tech. report CMU-RI-TR-03-08, Robotics Institute, Carnegie Mellon University, May, 2003

BibTeX Reference
@techreport{Liu_2003_4366,
   author = "Yanxi Liu and S. Mitra",
   title = "Human Identification versus Expression Classification via Bagging on Facial Asymmetry",
   booktitle = "",
   institution = "Robotics Institute",
   month = "May",
   year = "2003",
   number= "CMU-RI-TR-03-08",
   address= "Pittsburgh, PA",
}