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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Proceedings ArticleDOI
Learning from Dances: Pose-Invariant Re-Identification for Multi-Person Tracking
TL;DR: This work proposes an end-to-end deep learning framework Sparse-Temporal ReID Network, which not only realizes human pose disentanglement in an image recovery manner, but also makes efficient linkages between the identical subjects via a unique Sparse temporal identity sampling technique across time steps.
Journal ArticleDOI
The Untold Secrets of WiFi-Calling Services: Vulnerabilities, Attacks, and Countermeasures
TL;DR: It is disclosed that current Wi-Fi calling security is not bullet-proof and three vulnerabilities are uncovered, and two proof-of-concept attacks are devised: telephony harassment or denial of voice service and user privacy leakage; both can bypass the existing security defenses.
Journal ArticleDOI
PGM-face: Pose-guided margin loss for cross-pose face recognition
TL;DR: Compared with pose-robust face representation learning methods, the proposed Pose-Guided Margin Loss (PGM-Face) extends the dimensions of the linear transformation matrix for each class to estimate the head poses, therefore the learned features of each class are soft clustered guided by the head pose.
Journal ArticleDOI
Frontal-Centers Guided Face: Boosting Face Recognition by Learning Pose-Invariant Features
TL;DR: This work proposes a novel Frontal-Centers Guided Loss (FCGFace) to obtain highly discriminative features for face recognition, capable of adaptively adjusting the distribution of profile face features and narrowing the gap between them and frontal face features during different training stages to form compact identity clusters.
Proceedings ArticleDOI
A Hybrid Network for Facial Age Progression and Regression Learning
Rui-Cang Xie,Gee-Sern Jison Hsu +1 more
TL;DR: Experiments show that the proposed network can generates better facial age images with more age traits compared with other state-of-the-art approaches.
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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.