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Expression Classification using Wavelet Packet Method on Asymmetry Faces

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.