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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Lifting 2D StyleGAN for 3D-Aware Face Generation
TL;DR: Qualitative and quantitative results show the superiority of the approach over existing methods on 3D-controllable GANs in content controllability while generating realistic high quality images.
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Jointly De-biasing Face Recognition and Demographic Attribute Estimation
TL;DR: A novel de-biasing adversarial network (DebFace) that learns to extract disentangled feature representations for both unbiased face recognition and demographics estimation and a new scheme to combine demographics with identity features to strengthen robustness of face representation in different demographic groups is designed.
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Challenges in Disentangling Independent Factors of Variation
TL;DR: It is formally proved that without additional knowledge there is no guarantee that two images with the same factor of variation will be mapped to the same feature, which is called the reference ambiguity.
Proceedings ArticleDOI
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
TL;DR: In this paper, a classification-aware domain adversarial neural network is proposed to bring target examples into more classifiable regions of the source domain by using 3D geometry and image synthesis to preserve identity across pose transformations.
Journal ArticleDOI
3D Aided Duet GANs for Multi-View Face Image Synthesis
TL;DR: 3D aided duet generative adversarial networks (AD-GAN) to precisely rotate the yaw angle of an input face image to any specified angle is proposed to improve the visual realism of multi-view synthetic images but also preserves identity information well.
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