SPATIAL REGULARIZATION OF CANONICAL CORRELATION ANALYSIS FOR LOW-RESOLUTION FACE RECOGNITION

Canonical correlation analysis （CCA） based methods for low-resolution （LR） face recognition involve face images with different resolutions （or multi-resolutions）, i.e. LR and high-resolution （HR）. For single-reso- lution face recognition, researchers have shown that utilizing spatial information is beneficial to improving the rec- ognition accuracy, mainly because the pixels of each face are not independent but spatially correlated. However, for a multi-resolution scenario, there are no related works. Therefore, a method named spatial regularization of ca- nonical correlation analysis （SRCCA） is developed for LR face recognition to improve the performance of CCA by the regularization utilizing spatial information of different resolution faces. Furthermore, the impact of LR and HR spatial regularization terms on LR face recognition is analyzed through experiments.