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
Distortion-Disentangled Contrastive Learning
TL;DR: In this article , a distortion-disentangled contrastive learning (DDCL) framework is proposed to explicitly disentangle and exploit the distortion variant representation (DVR) inside the model and feature stream.
Book ChapterDOI
Pose Specification Based Online Person Identification
TL;DR: A weight system is used to record changes in posture and the sequence of frames that belong to the same person can be grouped and the undetected frames can be identified by the detected frames.
Proceedings ArticleDOI
Frontal Face Generation Based Multi-angle Face Identification System
TL;DR: Zhang et al. as discussed by the authors proposed a novel system to deal with multi-angle face identification in video sequence based on frontal face generation, which replaces the process of detection, alignment, and alignment in the typical face identification system.
Proceedings ArticleDOI
Conditional GAN for Small Datasets
TL;DR: Conditional FastGAN as discussed by the authors adds a condition vector to FastGAN to produce high-quality different domain images even on small datasets, and fine-tunes with manga face images to a model pre-trained with photo-only face images enabled control of generated images according to explicit conditions, such as photos and manga, for the same latent variables.
Journal ArticleDOI
Disentangled Representation Learning
TL;DR: The Disentangled Representation Learning (DRL) as discussed by the authors aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form.
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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.