Face Super Resolution in Reduced Spaces by Using Shape and Texture

The problem of inferring a missing face image which is at much higher resolution from lower observations is called as Face Super Resolution or Hallucination problem. Mostly the problem is approached in spatial domain by using the aligned textural information of the observation. However the ignorance of the shape information limits the performance of these approaches. In Resolution Aware Fitting (RAF) algorithm it was successfully shown that superior results could be obtained by utilizing both shape and texture components together. Though the RAF algorithm provides more satisfactory results, warping and deformation operations on high resolution image during the optimization could undermine its effectiveness in real world applications. As a remedy in this work we propose a faster alternative by effectively transforming the problem into reduced dimensions and making image warping only at low resolution. Experimentally it was shown that better reconstructions could be obtained faster than the RAF algorithm.