Eigenface-based super-resolution for face recognition

Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed that attempt to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing system to obtain a high resolution image that can later be passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose embedding the super-resolution algorithm into the face recognition system so that super-resolution is not performed in the pixel domain, but is instead performed in a reduced dimensional domain. The advantage of such an approach is a significant decrease in the computational complexity of the super-resolution algorithm because the algorithm no longer tries to construct a visually improved high quality image, but instead constructs the information required by the recognition algorithm directly in the lower dimensional domain without any unnecessary overhead.