Ying Wang
ABSTRACT
We address the challenges in automatic face recognition (AFR) applications when probe images present multiple variations including pose and resolution changes. Existing approaches attempt to seek a common feature space shared by these variations through linear or local linear mappings. In this paper, we leverage deep learning as a natural feature representation to discover intrinsic nonlinear relationships between images of multiple variations. Our method also extends the locality preserving projection (LPP) with nonlinear mappings learned through optimizing the objective function that preserves local neighboring structures between couterpart images. We perform the experiments on images from several available databases where only one frontal upright image presents in the gallery and variations on pose and resolution appear in the probe. The experiments show the superior recognition rates of our approach over the latest linear (or locally linear) methods.
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Index Terms
- Face recognition using nonlinear locality preserving with deep networks
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