Learning the Relationship Between High and Low Resolution Images in Kernel Space for Face Super Resolution


This paper proposes a new nonlinear face super resolution algorithm to address an important issue in face recognition from surveillance video namely, recognition of low resolution face image with nonlinear variations. The proposed method learns the nonlinear relationship between low resolution face image and high resolution face image in (nonlinear) kernel feature space. Moreover, the discriminative term can be easily included in the proposed framework. Experimental results on CMU-PIE and FRGC v2.0 databases show that proposed method outperforms existing methods as well as the recognition based on high resolution images.


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