Facial asymmetry quantification for expression invariant human identification

Author

Liu, Y. ; Schmidt, K.L. ; Cohn, J.F. ; Weaver, R.L.

Author_Institution

Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA

fYear

2002

Firstpage

198

Lastpage

204

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 (T. Kanade et al., 1999) and Feret (P.J. Phillips et al., 1998))

Keywords

biometrics (access control); face recognition; visual databases; AsymFaces; Cohn-Kanade; Eigen-face methods; Feret; Fisherface methods; biometric; discriminative information; expression invariant human identification; expression variations; facial asymmetry measures; facial asymmetry quantification; image data; publicly available face databases; quantified statistical facial asymmetry; Face recognition; Humans;

fLanguage

English

Publisher

ieee

Conference_Titel

Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on

Conference_Location

Washington, DC

Print_ISBN

0-7695-1602-5

Type

conf

DOI

10.1109/AFGR.2002.1004156

Filename

1004156

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1629263