Identity and Attribute Preserving Thumbnail Upscaling
This addresses the issue of identity loss and bias in face upscaling for applications like image enhancement, though it appears incremental by building on existing methods.
The paper tackles the problem of upscaling low-resolution thumbnail images of people to higher resolution while preserving identity and attributes like race and facial expressions, achieving improvements in face similarity recognition and lookalike generation.
We consider the task of upscaling a low resolution thumbnail image of a person, to a higher resolution image, which preserves the person's identity and other attributes. Since the thumbnail image is of low resolution, many higher resolution versions exist. Previous approaches produce solutions where the person's identity is not preserved, or biased solutions, such as predominantly Caucasian faces. We address the existing ambiguity by first augmenting the feature extractor to better capture facial identity, facial attributes (such as smiling or not) and race, and second, use this feature extractor to generate high-resolution images which are identity preserving as well as conditioned on race and facial attributes. Our results indicate an improvement in face similarity recognition and lookalike generation as well as in the ability to generate higher resolution images which preserve an input thumbnail identity and whose race and attributes are maintained.