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Title: High-quality face image generated with conditional boundary equilibrium generative adversarial networks
Authors: Huang, Bin
Chen, Weihai
Wu, Xingming
Lin, Chun-Liang
Suganthan, Ponnuthurai Nagaratnam
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Huang, B., Chen, W., Wu, X., Lin, C.-L., & Suganthan, P. N. (2018). High-quality face image generated with conditional boundary equilibrium generative adversarial networks. Pattern Recognition Letters, 111, 72-79. doi:10.1016/j.patrec.2018.04.028
Journal: Pattern Recognition Letters
Abstract: We propose a novel single face image super-resolution method, which is named Face Conditional Generative Adversarial Network (FCGAN), based on boundary equilibrium generative adversarial networks. Without taking any prior facial information, our approach combines the pixel-wise L1 loss and GAN loss to optimize our super-resolution model and to generate a high-quality face image from a low-resolution one robustly (with upscaling factor 4 ×). Additionally, Compared with existing peer researches, both training and testing phases of FCGAN are end-to-end pipeline without pre/post-processing. To enhance the convergence speed and strengthen feature propagation, the Generator and Discriminator networks are designed with a skip-connection architecture, and both using an auto-encoder structure. Quantitative experiments demonstrate that our model achieves competitive performance compared with the state-of-the-art models based on both visual quality and quantitative criterions. We believe this high-quality face image generated method can impact many applications in face identification and intelligent monitor.
ISSN: 0167-8655
DOI: 10.1016/j.patrec.2018.04.028
Rights: © 2018 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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