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
More filters
Journal ArticleDOI
iFace 1.1: A Proof-of-Concept of a Facial Authentication Based Digital ID for Smart Cities
TL;DR: This paper proposes a proof-of-concept of a facial authentication-based end-to-end digital ID system for a smart city, designed to detect the first type of attack, especially deepfake and presentation attacks.
Book ChapterDOI
An Overview of Fake Face Detection Approaches
TL;DR: In this article, the authors investigated the abundantly used fake face generation models and examined the techniques for detecting facial manipulation using deep face understanding systems and machine learning techniques, but only a few studies to detect the generated face from real.
Proceedings ArticleDOI
Generative Adversarial Nets for Cost-Sensitive Face Recognition
TL;DR: First, generate frontal face images using the two-channel generative adversarial nets, and then introduce cost-sensitive learning in the recognition process to consider the cost imbalance problem.
Book ChapterDOI
A Competition of Shape and Texture Bias by Multi-view Image Representation
TL;DR: In this article, the authors try to explore the power of CNN and reconcile the hypothesis contradiction of CNNs from a multi-view image representation by segmenting and recombining the object shape, texture and image background through two losses: image reconstructed loss and feature discrepancy loss.
Posted Content
Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images
TL;DR: Li et al. as discussed by the authors proposed a fingerprint estimation network to estimate a GM fingerprint from a generated image by training with four constraints to encourage the fingerprint to have desired properties, and a Parsing Network to predict network architecture and loss functions from the estimated fingerprints.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Journal ArticleDOI
Representation Learning: A Review and New Perspectives
TL;DR: Recent work in the area of unsupervised feature learning and deep learning is reviewed, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks.
Proceedings ArticleDOI
FaceNet: A unified embedding for face recognition and clustering
TL;DR: A system that directly learns a mapping from face images to a compact Euclidean space where distances directly correspond to a measure offace similarity, and achieves state-of-the-art face recognition performance using only 128-bytes perface.
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.