Super-Resolution Method for Multiview Face Recognition From a Single Image Per Person Using Nonlinear Mappings on Coherent Features

In video surveillance, the face recognition usually aims at recognizing a nonfrontal low resolution face image from the gallery in which each person has only one high resolution frontal face image. Traditional face recognition approaches have several challenges, such as the difference of image resolution, pose variation and only one gallery image per person. This letter presents a regression based method that can successfully recognize the identity given all these difficulties. The nonlinear regression models from the specific nonfrontal low resolution image to frontal high resolution features are learnt by radial basis function in subspace built by canonical correlation analysis. Extensive experiments on benchmark database show the superiority of our method.