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Facial Asymmetry Quantification for Expression Invariant Human Identification

By Human Identification, Y. Liu, K. L. Schmidt, J.F. Cohn and R. L. Weaver

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 publically available face databases (Cohn-Kanade and Feret)

Year: 2002
OAI identifier: oai:CiteSeerX.psu:10.1.1.18.6625
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