Conventional face hallucination methods rely heavily on accurate alignment of low-resolution (LR) faces before upsampling them. Misalignment often leads to deficient results and unnatural artifacts for large upscaling factors. However, due to the diverse range of poses and different facial expressions, aligning an LR input image, in particular when it is tiny, is severely difficult. To overcome this challenge, here we present an end-to-end transformative discriminative neural network (TDN) devised for super-resolving unaligned and very small face images with an extreme upscaling factor of 8. Our method employs an upsampling network where we embed spatial transformation layers to allow local receptive fields to line-up with similar spatial supports. Furthermore, we incorporate a class-specific loss in our objective through a successive discriminative network to improve the alignment and upsampling performance with semantic information. Extensive experiments on large face datasets show that the proposed method significantly outperforms the state-of-the-art.
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