Reconstruction of high-resolution facial images for visual surveillance

In this chapter we provide a summary of our previous works concerning the reconstruction of high-resolution facial images for visual surveillance. Specifically we present our methods of reconstructing high-resolution facial image from a low-resolution facial image based on example-based learning and iterative error back-projection. In our method, 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. Moreover iterative error back-projection is applied to improve the result of high-resolution reconstruction. The encouraging results of our methods show that our high-resolution reconstruction methods can be used to improve the performance of the face recognition by enhancing the resolution of low-resolution facial images captured in visual surveillance systems.