Multi-modal face image super-resolutions in tensor space

Face images of non-frontal views under poor illumination with low resolution reduce dramatically face recognition accuracy. To overcome these problems, super-resolution techniques can be exploited. In this paper, we present a Bayesian framework to perform multi-modal (such as variations in viewpoint and illumination) face image super-resolutions in tensor space. Given a single modal low-resolution face image, we benefit from the multiple factor interactions of training tensor, and super-resolve its high-resolution reconstructions across different modalities. Instead of performing pixel-domain super-resolutions, we reconstruct the high-resolution face images by computing a maximum likelihood identity parameter vector in high-resolution tensor space. Experiments show promising results of multi-view and multi-illumination face image super-resolutions respectively.