A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
2010 IEEE International Conference on Acoustics, Speech and Signal Processing
For the problem of low recognition rate on low resolution face images, a super-resolution method for face recognition based on correlated features and nonlinear mappings is proposed in this paper. Canonical correlation analysis (CCA) is applied to establish the correlated subspaces between the features of high and low resolution face images, and radial base functions (RBFs) are employed to construct the nonlinear mappings between the features in the correlated subspaces. Finally, thedoi:10.1109/icassp.2010.5495615 fatcat:ms2meoyotfb2tbifxx74rabh54