Enhancing low-resolution facial images using error back-projection for human identification at a distance

This paper proposes a new method of enhancing the resolution of low-resolution facial images using an error back-projection method based on a top-down 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 obtained by using the optimal coefficients for linear combination of the high-resolution prototypes. In addition, an error back-projection procedure is applied to improve the accuracy of resolution enhancement.