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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Deep Gait Recognition: A Survey.
TL;DR: A comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning can be found in this paper, where the authors present broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions.
Proceedings ArticleDOI
Triplet Angular Loss for Pose-Robust Face Recognition
TL;DR: Zhang et al. as discussed by the authors proposed to learn the pose-robust face representation by normalizing the profile face in feature level directly and jointly considering both intra-class compactness and inter-class separability.
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ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesis
TL;DR: Zhang et al. as discussed by the authors proposed a novel identity-aware and capsule-enhanced generative adversarial network with graph-based reasoning (ICE-GAN), which introduced micro-expression synthesis as an auxiliary task to assist recognition.
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K-FACE: A Large-Scale KIST Face Database in Consideration with Unconstrained Environments.
TL;DR: The K-FACE database as discussed by the authors contains more than 1 million high-quality images of 1,000 subjects selected by considering the ratio of gender and age groups, including 27 poses, 35 lighting conditions, three expressions, and occlusions by the combination of five types of accessories.
Journal ArticleDOI
Disentangled Representations for Short-Term and Long-Term Person Re-Identification
TL;DR: Li et al. as discussed by the authors disentangle identity-related and -unrelated features from person images through an identity-shuffling technique that exploits identification labels alone without any auxiliary supervisory signals.
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