Face Recognition at a Distance: a study of super resolution
Yuxi Peng · 2011
We evaluate the performance of face recognition using images with di�er- ent resolution. The experiments are conducted on Face Recognition Grand Challenge version one (FRGC v1.0) database and Surveillance Cameras Face (SCface) Database. Three recognition methods are used, namely Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP). To improve the performance of face images with low- resolution (LR), two state-of-art super-resolution (SR) methods are applied. One is called Discriminative Super-resolution (DSR). It �nds the relation- ship from low-resolution images to their corresponding high-resolution (HR) images so that the reconstructed super-resolution images would be close to the HR images which belongs to the same subject with them and far away from others. The other SR method uses Nonlinear Mappings on Coherent Features (NMCF). Canonical Correlation analysis is applied to compute the coherent features between the PCA features of HR and LR images. Then Radial Basis Functions (RBFs) is used to �nd the mapping from LR fea- tures to HR features in the coherent feature space. The two SR methods are compared on both FRGC and SCface databases as well.