Super-resolution reconstruction by learning can better describe image details and significantly enhance image resolution

thus improving the visual effect of the image because of the introduction of priori knowledge. Applying super-resolution reconstruction to sketch face recognition does not only improve the quality of the image but also effectively increases the recogniton rate. First

eigenface algorithm is used to synthesize a photo according to the input sketch. Then

super-resolution reconstruction via sparse representation is executed on the synthesized photo. Finally

principal component analysis is employed to recognize the synthesized photos that have been formed before the reconstruction and after. The experiment is performed on CUHK Face Sketch Database (CUFS). Experimental results show that

after super-resolution reconstruction

the synthesized photo can describe the facial details better

such as the eyes. Moreover

because of the introduction of priori knowledge

the sketch face recognition rate is improved after reconstruction. Experimental results also indicate that the recognition rate of support vector machine algorithm is improved from 65% to 66%

and the recognition rate of the principal component analysis algorithm is improved from 87% to 89%. Sketch face recognition based on super-resolution reconstruction can improve the image visual effect and increase the sketch face recognition rate effectively.