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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Journal ArticleDOI
Semi-Supervised Face Frontalization in the Wild
TL;DR: This article presents a Cycle-Consistent Face Frontalization Generative Adversarial Network (CCFF-GAN) which consists of both the supervised and the unsupervised components and uses the indoor paired (labeled) data to learn a roughly accurate frontalization network which may not generalize well to outdoor (in-the-wild) scenarios.
Journal ArticleDOI
Identity-and-pose-guided generative adversarial network for face rotation
TL;DR: Li et al. as discussed by the authors proposed an identity-and-pose-guided generative adversarial network (IPG-GAN) to generate faces with arbitrary head poses.
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Biphasic Learning of GANs for High-Resolution Image-to-Image Translation
TL;DR: This work presents a novel training framework for GANs, namely biphasic learning, to achieve image-to-image translation in multiple visual domains at $1024^2$ resolution and proposes a novel inherited adversarial loss to achieve the enhancement of model capacity and stabilize the training phase transition.
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Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation
TL;DR: OroJaR as discussed by the authors encourages the variation of output caused by perturbations on different latent dimensions to be orthogonal, and the Jacobian with respect to the input is calculated to represent this variation.
Journal ArticleDOI
Biometrics: Trust, But Verify
TL;DR: In this article , the authors provide an overview of biometric recognition systems design issues and how the biometric community can address these issues to better instill trust, fairness, and security for all.
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