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
Face recognition: Past, present and future (a review)
TL;DR: The methods used to obtain and classify facial biometric data in the literature have been summarized and a taxonomy of image-based and video-based face recognition methods is given, outlining the major historical developments, and the main processing steps.
Journal ArticleDOI
Identity-aware CycleGAN for face photo-sketch synthesis and recognition
TL;DR: An Identity-Aware CycleGAN (IACycleGAN) model is proposed that applies a new perceptual loss to supervise the image generation network and improves CycleGAN on photo-sketch synthesis by paying more attention to the synthesis of key facial regions, such as eyes and nose, which are important for identity recognition.
Book ChapterDOI
Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling
TL;DR: A cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations, and then use the distilled physiological features for robust multi-task physiological measurements.
Book ChapterDOI
Synthetically Supervised Feature Learning for Scene Text Recognition
TL;DR: This work designs a multi-task network with an encoder-discriminator-generator architecture to guide the feature of the original image toward that of the clean image, and significantly outperforms the state-of-the-art methods on standard scene text recognition benchmarks in the lexicon-free category.
Proceedings ArticleDOI
Rotate-and-Render: Unsupervised Photorealistic Face Rotation From Single-View Images
TL;DR: In this paper, the authors propose a novel unsupervised framework that can synthesize photo-realistic rotated faces using only single-view image collections in the wild, which can serve as a strong self-supervision.
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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.