Improving human perception of GAN generated facial image synthesis by filtering the training set considering facial attributes

Abstract With recent advances in deep learning research, generative models have achieved great achievements and play an increasingly important role in current industrial applications. At the same time, technologies derived from generative methods are also under a wide discussion with researches, such as style transfer, image synthesis and so on. In this work, we treat generative methods as a possible solution to medical image augmentation. We proposed a context-aware generative framework, which can successfully change the gray scale of CT scans but almost without any semantic loss. By producing target images that with specific style / distribution, we greatly increased the robustness of segmentation model after adding generations into training set. Besides, we improved 2– 4% pixel segmentation accuracy over original U-NET in terms of spine segmentation. Lastly, we compared generations produced by networks when using different feature extractors (Vgg, ResNet and DenseNet) and made a detailed analysis on their performances over style transfer.

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Facial Image Synthesis

Computer Facial Animation ◽

10.1201/b10705-8 ◽

2008 ◽

pp. 193-234

Keyword(s):

Image Synthesis ◽

Facial Image

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Dual Semi-Supervised Learning for Facial Action Unit Recognition

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018827 ◽

2019 ◽

Vol 33 ◽

pp. 8827-8834 ◽

Cited By ~ 1

Author(s):

Guozhu Peng ◽

Shangfei Wang

Keyword(s):

Auxiliary Information ◽

Image Synthesis ◽

Training Data ◽

Facial Image ◽

Generative Adversarial Network ◽

Informative Feedback ◽

Action Unit ◽

Facial Action ◽

The Face ◽

Facial Images

Current works on facial action unit (AU) recognition typically require fully AU-labeled training samples. To reduce the reliance on time-consuming manual AU annotations, we propose a novel semi-supervised AU recognition method leveraging two kinds of readily available auxiliary information. The method leverages the dependencies between AUs and expressions as well as the dependencies among AUs, which are caused by facial anatomy and therefore embedded in all facial images, independent on their AU annotation status. The other auxiliary information is facial image synthesis given AUs, the dual task of AU recognition from facial images, and therefore has intrinsic probabilistic connections with AU recognition, regardless of AU annotations. Specifically, we propose a dual semi-supervised generative adversarial network for AU recognition from partially AU-labeled and fully expressionlabeled facial images. The proposed network consists of an AU classifier C, an image generator G, and a discriminator D. In addition to minimize the supervised losses of the AU classifier and the face generator for labeled training data, we explore the probabilistic duality between the tasks using adversary learning to force the convergence of the face-AU-expression tuples generated from the AU classifier and the face generator, and the ground-truth distribution in labeled data for all training data. This joint distribution also includes the inherent AU dependencies. Furthermore, we reconstruct the facial image using the output of the AU classifier as the input of the face generator, and create AU labels by feeding the output of the face generator to the AU classifier. We minimize reconstruction losses for all training data, thus exploiting the informative feedback provided by the dual tasks. Within-database and cross-database experiments on three benchmark databases demonstrate the superiority of our method in both AU recognition and face synthesis compared to state-of-the-art works.

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