WGAN主要从损失函数的角度对GAN做了改进,损失函数改进之后的WGAN即使在全链接层上也能得到很好的表现结果。

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论文题目

基于CGAN的人脸深度图估计: Face Depth Estimation With Conditional Generative Adversarial Networks

论文摘要

深度图的估计和单幅或多幅人脸图像的三维重建是计算机视觉的一个重要研究领域。在过去十年中提出和发展了许多方法。然而,像健壮性这样的问题仍然需要通过进一步的研究来解决。随着GPU计算方法的出现,卷积神经网络被应用到许多计算机视觉问题中。后来,条件生成对抗网络(CGAN)因其易于适应许多图像间问题而受到关注。CGANs已被广泛应用于各种任务,如背景掩蔽、分割、医学图像处理和超分辨率。在这项工作中,我们开发了一种基于GAN的方法来估计任何给定的单人脸图像的深度图。许多GANs的变体已经被测试用于的深度估计这项工作任务。我们的结论是,条件式瓦瑟斯坦GAN结构提供了最稳健的方法。我们还将该方法与其它两种基于深度学习和传统方法的方法进行了比较,实验结果表明,WGAN为从人脸图像中估计人脸深度图提供了很好的机会。

论文作者

Abdullah Taha Arslan,Erol Seke

关键字

三维人脸重建,生成对抗网络,深度学习

百度链接

链接: https://pan.baidu.com/s/13zk5uEeuGw7f5VyL9xAong 密码: 2bgb

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Biometric has been increasing in relevance these days since it can be used for several applications such as access control for instance. Unfortunately, with the increased deployment of biometric applications, we observe an increase of attacks. Therefore, algorithms to detect such attacks (Presentation Attack Detection (PAD)) have been increasing in relevance. The LivDet-2020 competition which focuses on Presentation Attacks Detection (PAD) algorithms have shown still open problems, specially for unknown attacks scenarios. In order to improve the robustness of biometric systems, it is crucial to improve PAD methods. This can be achieved by augmenting the number of presentation attack instruments (PAI) and bona fide images that are used to train such algorithms. Unfortunately, the capture and creation of presentation attack instruments and even the capture of bona fide images is sometimes complex to achieve. This paper proposes a novel PAI synthetically created (SPI-PAI) using four state-of-the-art GAN algorithms (cGAN, WGAN, WGAN-GP, and StyleGAN2) and a small set of periocular NIR images. A benchmark between GAN algorithms is performed using the Frechet Inception Distance (FID) between the generated images and the original images used for training. The best PAD algorithm reported by the LivDet-2020 competition was tested for us using the synthetic PAI which was obtained with the StyleGAN2 algorithm. Surprisingly, The PAD algorithm was not able to detect the synthetic images as a Presentation Attack, categorizing all of them as bona fide. Such results demonstrated the feasibility of synthetic images to fool presentation attacks detection algorithms and the need for such algorithms to be constantly updated and trained with a larger number of images and PAI scenarios.

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