Abstract
A novel nonlinear face recognition method named GPPface is proposed in this paper. GPPface is based on nonlinear dimensionality reduction algorithm, Geodesic Preserving Projection (GPP). As face images are regarded to be embedded in a nonlinear space, GPP is presented to nonlinearly map high-dimensional face images to low-dimensional feature space. GPP overcomes the weaknesses of traditional linear and nonlinear dimensionality reduction algorithms, well preserves the intrinsic structure of the manifold and can fast and efficiently map new sample point to feature space. To recover space structure of face images and tackle small sample size problem, 3D morphable model is developed to derive multiple images of a person from a single image. Experimental results on ORL and PIE face databases show that our method makes impressive performance improvement compared with conventional face recognition methods.
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