Robotics Institute: Human Identification versus Expression Classification via Bagging on Facial Asymmetry

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, April, 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, April, 2003

BibTeX Reference

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

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