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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Book ChapterDOI
Learning Flow-based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision
TL;DR: Wang et al. as discussed by the authors proposed a Flow-based Feature Warping Model (FFWM), which can learn to synthesize photo-realistic and illumination preserving frontal images with illumination inconsistent supervision.
Journal ArticleDOI
Pose-invariant face recognition with multitask cascade networks
TL;DR: In this paper , a multi-task face recognition method is proposed for face under pose variations using a multitask convolutional neural network (CNN) and a pose estimation method followed by a face identification module is combined in a cascaded structure and used separately.
Proceedings ArticleDOI
Recent Progress of Face Image Synthesis
TL;DR: A comprehensive review of typical face synthesis works that involve traditional methods as well as advanced deep learning approaches is provided in this article, where Generative Adversarial Networks (GANs) are highlighted to generate photo-realistic and identity preserving results.
Posted Content
GAGAN: Geometry-Aware Generative Adversarial Networks
TL;DR: Experimental results on face generation indicate that the GAGAN can generate realistic images of faces with arbitrary facial attributes such as facial expression, pose, and morphology, that are of better quality than current GAN-based methods.
Proceedings ArticleDOI
Fully Understanding Generic Objects: Modeling, Segmentation, and Reconstruction
Feng Liu,Luan Tran,Xiaoming Liu +2 more
TL;DR: In this article, a semi-supervised learning approach is proposed to infer 3D structure of a generic object from a 2D image by decomposing it into latent representations of category, shape, albedo, lighting and camera projection matrix, decode the representations to segmented 3D shape and albedos respectively and fuse these components to render an image well approximating the input image.
References
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