Identity and Attribute Preserving Thumbnail Upscaling
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 30 May 2021.
Builds on
10 of 18 references.
References · 10
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
- 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,245
- 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,204
- 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
- 2016MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognitioncited by 624
- 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
- 2014Deep Learning Face Representation by Joint Identification-Verificationcited by 162
- 2019FairFace: Face Attribute Dataset for Balanced Race, Gender, and Agecited by 87
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Citing papers per year: 2023: 1.
1 paper.