Facial Asymmetry Quantification for Expression Invariant Human Identification
Conference Paper, Proceedings of 5th IEEE International Conference on Automatic Face and Gesture Recognition (FG '02), pp. 198 - 204, May, 2002
Abstract
We investigate the effect of quantified statistical facial asymmetry as a biometric under expression variations. Our findings 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 publicly available face databases (Cohn-Kanade and Feret).
BibTeX
@conference{Liu-2002-8418,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 5th IEEE International Conference on Automatic Face and Gesture Recognition (FG '02)},
year = {2002},
month = {May},
pages = {198 - 204},
}
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