Expression Classification using Wavelet Packet Method on Asymmetry Faces

Kenny Teng and Yanxi Liu
tech. report CMU-RI-TR-06-03, Robotics Institute, Carnegie Mellon University, January, 2006


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Abstract
We are investigating wavelet packet method on asymmetry faces for expression classification. Two types of quantified asymmetry measures have already been defined by Liu et al in 2002, but they are computed only in the image intensity domain. We are proposing a novel approach to extend those asymmetry faces by using wavelet transforms to achieve better and more robust results. One of the most interesting characteristics of wavelet transforms is their ability to represent a signal into partitions of time-frequency plane. Using a random subset of 3 expressions with 55 subjects each from a normalized version of the Cohn-Kanade AU-Coded Facial Expression Database, error reduction rates of 25-95% have been achieved. Our findings show that asymmetry faces have some features that remain more consistent and discriminative in the wavelet subspaces than in the image intensity domain. We also show that certain wavelet subspaces will contribute more than others to classification accuracy.

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
Number of pages: 23

Text Reference
Kenny Teng and Yanxi Liu, "Expression Classification using Wavelet Packet Method on Asymmetry Faces," tech. report CMU-RI-TR-06-03, Robotics Institute, Carnegie Mellon University, January, 2006

BibTeX Reference
@techreport{Liu_2006_5300,
   author = "Kenny Teng and Yanxi Liu",
   title = "Expression Classification using Wavelet Packet Method on Asymmetry Faces",
   booktitle = "",
   institution = "Robotics Institute",
   month = "January",
   year = "2006",
   number= "CMU-RI-TR-06-03",
   address= "Pittsburgh, PA",
}