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
Improving Face Recognition from Hard Samples via Distribution Distillation Loss
Yuge Huang,Pengcheng Shen,Ying Tai,Shaoxin Li,Xiaoming Liu,Jilin Li,Feiyue Huang,Rongrong Ji +7 more
TL;DR: DDLiu et al. as discussed by the authors adopted state-of-the-art classifiers such as Arcface to construct two similarity distributions: a teacher distribution from easy samples and a student distribution from hard samples.
Posted Content
TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition
TL;DR: A Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition.
Posted Content
On Disentangling Spoof Trace for Generic Face Anti-Spoofing
TL;DR: This work designs a novel adversarial learning framework to disentangle the spoof traces from input faces as a hierarchical combination of patterns at multiple scales, which demonstrates superior spoof detection performance on both seen and unseen spoof scenarios while providing visually convincing estimation of spoof traces.
Journal ArticleDOI
Deformable Face Net for Pose Invariant Face Recognition
TL;DR: The proposed Deformable Face Net can effectively handle pose invariant face recognition (PIFR) and outperforms the state-of-the-art methods, especially on the datasets with large poses.
Journal ArticleDOI
PrivacyNet: Semi-Adversarial Networks for Multi-Attribute Face Privacy
TL;DR: Extensive experiments using multiple face matchers, multiple age/gender/race classifiers, and multiple face datasets demonstrate the generalizability of the proposed multi-attribute privacy enhancing method across multiple face and attribute classifiers.
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