A framework of 2D Fisher discriminant analysis: application to face recognition with small number of training samples

• Published in: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 2; pp. 1083 - 1088 vol. 2
• Main Authors: Kong, H., Wang, L., Teoh, E.K., Wang, J.-G., Venkateswarlu, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Covariance matrix; Eigenvalues and eigenfunctions; Face recognition; Feature extraction; Image databases; Linear discriminant analysis; Null space; Principal component analysis; Scattering; Vectors

A novel framework called 2D Fisher discriminant analysis (2D-FDA) is proposed to deal with the small sample size (SSS) problem in conventional one-dimensional linear discriminant analysis (1D-LDA). Different from the 1D-LDA based approaches, 2D-FDA is based on 2D image matrices rather than column ve...