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
UAV Flight State Recognition Using AC-GAN Based Method
TL;DR: An Auxiliary Classifier Generative Adversarial Network (AC-GAN) based method is proposed that supplements samples by generating samples that enjoy the same distribution with the real data, thereby improving the accuracy of the recognition.
Proceedings ArticleDOI
Multimodal Asymmetric Dual Learning for Unsupervised Eyeglasses Removal
Qing Lin,Bo Yan,Weimin Tan +2 more
TL;DR: Zhang et al. as discussed by the authors proposed a multimodal asymmetric dual learning method for unsupervised glasses removal, which uses large-scale face images with and without glasses for dual feature learning.
Book ChapterDOI
Face Recognition Using Sf 3 CNN with Higher Feature Discrimination
TL;DR: In this paper, a 3D Residual Network (3D ResNet) and A-Softmax loss are used to extract highly discriminative features from the video for face recognition.
Proceedings Article
Demodalizing Face Recognition with Synthetic Samples
Zhonghua Zhai,Pengju Yang,Xiaofeng Zhang,Maji Huang,Haijing Cheng,Yan Xuejun,Chunmao Wang,Shiliang Pu +7 more
TL;DR: In this article, the authors propose a demodalizing face recognition training architecture for the first time and provide a feasible method for recognition training using synthetic samples, which gradually reveal a generated modality that is difficult to quantify or describe.
Journal ArticleDOI
Facial Pose and Expression Transfer Based on Classification Features
TL;DR: Zhang et al. as discussed by the authors proposed a generative adversarial network model based on classification features for facial pose and facial expression transfer, which achieved state-of-the-art performance on two datasets.
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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.