Gauss–Laguerre wavelet textural feature fusion with geometrical information for facial expression identification
1 Department of Electrical and Computer Engineering, University of Calgary, Canada ENA221, ICT Building, 2500 University Drive NW, Calgary, T2N 1N4, Canada
2 Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
EURASIP Journal on Image and Video Processing 2012, 2012:17 doi:10.1186/1687-5281-2012-17Published: 25 September 2012
Facial expressions are a valuable source of information that accompanies facial biometrics. Early detection of physiological and psycho-emotional data from facial expressions is linked to the situational awareness module of any advanced biometric system for personal state re/identification. In this article, a new method that utilizes both texture and geometric information of facial fiducial points is presented. We investigate Gauss–Laguerre wavelets, which have rich frequency extraction capabilities, to extract texture information of various facial expressions. Rotation invariance and the multiscale approach of these wavelets make the feature extraction robust. Moreover, geometric positions of fiducial points provide valuable information for upper/lower face action units. The combination of these two types of features is used for facial expression classification. The performance of this system has been validated on three public databases: the JAFFE, the Cohn-Kanade, and the MMI image.