Reconstruction of high-resolution facial image using recursive error back-projection

This paper proposes a new method of reconstructing high-resolution facial image from a low-resolution facial image using a recursive error back-projection of example-based learning. A face is represented by a linear combination of prototypes of shape and texture. With the shape and texture information about the pixels in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those of texture by solving least square minimization. Then high-resolution facial image can be reconstructed by using the optimal coefficients for linear combination of the high-resolution prototypes. Moreover recursive error back-projection is applied to improve the accuracy of high-resolution reconstruction. An error back-projection is composed of estimation, simulation, and error compensation. The encouraging results of the proposed method show that our method can be used to improve the performance of the face recognition by applying our method to enhance the low-resolution facial images captured at visual surveillance systems.