Conference Paper

Face Recognition Based on Discriminant Evaluation in the Whole Space

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ.
DOI: 10.1109/ICASSP.2007.366218 Conference: Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, Volume: 2
Source: IEEE Xplore

ABSTRACT This paper proposes a face recognition approach that performs linear discriminant analysis in the whole eigenspace. It decomposes the eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unstable subspace due to finite number of training samples. Eigenvalues in the unstable subspace are replaced by a constant. This alleviates the over-fitting problem and enables the discriminant evaluation in the whole space. Feature extraction or dimensionality reduction occurs only at the final stage after the discriminant assessment. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results comparing some popular subspace methods on FERET and ORL databases show that our approach consistently outperforms others.

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