Proceedings ArticleDOI
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition
Luan Tran,Xi Yin,Xiaoming Liu +2 more
- pp 1283-1292
TLDR
Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.Abstract:
The large pose discrepancy between two face images is one of the key challenges in face recognition. Conventional approaches for pose-invariant face recognition either perform face frontalization on, or learn a pose-invariant representation from, a non-frontal face image. We argue that it is more desirable to perform both tasks jointly to allow them to leverage each other. To this end, this paper proposes Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with three distinct novelties. First, the encoder-decoder structure of the generator allows DR-GAN to learn a generative and discriminative representation, in addition to image synthesis. Second, this representation is explicitly disentangled from other face variations such as pose, through the pose code provided to the decoder and pose estimation in the discriminator. Third, DR-GAN can take one or multiple images as the input, and generate one unified representation along with an arbitrary number of synthetic images. Quantitative and qualitative evaluation on both controlled and in-the-wild databases demonstrate the superiority of DR-GAN over the state of the art.read more
Citations
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Proceedings ArticleDOI
Facial Expression Recognition in the Wild: A Cycle-Consistent Adversarial Attention Transfer Approach
TL;DR: Quantitative and qualitative evaluations on two challenging in-the-wild datasets demonstrate that the proposed Cycle-consistent adversarial Attention Transfer model (CycleAT) for simultaneous facial image synthesis and facial expression recognition in the wild performs favorably against state-of- the-art methods.
Proceedings ArticleDOI
Identity-Preserving Face Anonymization via Adaptively Facial Attributes Obfuscation
TL;DR: Zhang et al. as mentioned in this paper proposed a face anonymization framework that obfuscates visual appearance while preserving the identity discriminability, which is composed of two parts: an identity-aware region discovery module and an identityaware face confusion module.
Journal ArticleDOI
A survey on face data augmentation for the training of deep neural networks
Xiang Wang,Kai Wang,Lian Shiguo +2 more
TL;DR: In this article, the authors reviewed the existing works of face data augmentation from the perspectives of the transformation types and methods, with the state-of-the-art approaches involved.
Journal ArticleDOI
Robust Multimodal Representation Learning With Evolutionary Adversarial Attention Networks
TL;DR: A deep learning-based approach named Evolutionary Adversarial Attention Networks (EAAN) is proposed, which combines the attention mechanism with adversarial networks through evolutionary training, for robust multimodal representation learning.
Journal ArticleDOI
Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets
TL;DR: A novel deep generative model able to produce face videos from a given image of a neutral face and a label indicating a specific facial expression, e.g. spontaneous smile is proposed.
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Posted Content
Conditional Generative Adversarial Nets
Mehdi Mirza,Simon Osindero +1 more
TL;DR: The conditional version of generative adversarial nets is introduced, which can be constructed by simply feeding the data, y, to the generator and discriminator, and it is shown that this model can generate MNIST digits conditioned on class labels.