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Tiny face hallucination via boundary equilibrium generative adversarial networks
Author(s): Wen-Ze Shao; Jing-Jing Xu; Long Chen; Qi Ge; Li-Qian Wang; Bing-Kun Bao; Hai-Bo Li
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Paper Abstract

It is known that actual performance of most previous face hallucination approaches will drop dramatically as a very low-resolution tiny face is provided. Inspired by the latest progress in deep unsupervised learning, this paper works on tiny faces of size 16×16 pixels and magnifies them into their 8× upsampling ones by exploiting the boundary equilibrium generative adverarial networks (BEGAN). Besides imposing a pixel-wise L2 regularization term to the generative model, it is found that our targeted auto-encoding generator with residual blocks and skip connections is a key component for BEGAN achieving state-of-the-art hallucination performance. The cropped CelebA face dataset is preliminarily used in our experiments. The results demonstrate that the proposed approach is not only of fast and stable convergence, but also robust to pose, expression, illuminance and occluded variations.

Paper Details

Date Published: 6 May 2019
PDF: 11 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110693M (6 May 2019); doi: 10.1117/12.2524361
Show Author Affiliations
Wen-Ze Shao, Nanjing Univ. of Posts and Telecommunications (China)
Jing-Jing Xu, Nanjing Univ. of Posts and Telecommunications (China)
Long Chen, Nanjing Univ. of Posts and Telecommunications (China)
Qi Ge, Nanjing Univ. of Posts and Telecommunications (China)
Li-Qian Wang, Nanjing Univ. of Posts and Telecommunications (China)
Bing-Kun Bao, Nanjing Univ. of Posts and Telecommunications (China)
Hai-Bo Li, Nanjing Univ. of Posts and Telecommunications (Sweden)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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