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
Noise Modeling, Synthesis and Classification for Generic Object Anti-Spoofing
TL;DR: This work defines and tackles the problem of Generic Object Anti-Spoofing (GOAS), and proposes a GAN-based architecture to synthesize and identify the noise patterns from seen and unseen medium/sensor combinations, and demonstrates the procedure of synthesis and identification are mutually beneficial.
Proceedings ArticleDOI
Conditional Adversarial Generative Flow for Controllable Image Synthesis
TL;DR: In this article, a conditional adversarial generative flow (CAGlow) model is proposed to learn an encoder to estimate the mapping from condition space to latent space in an adversarial manner.
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DebFace: De-biasing Face Recognition
TL;DR: A novel de-biasing adversarial network that learns to extract disentangled feature representations for both unbiased face recognition and demographics estimation and a new scheme to combine demographics with identity features to strengthen robustness of face representation in different demographic groups is designed.
Journal ArticleDOI
An Adversarial Neuro-Tensorial Approach for Learning Disentangled Representations
Mengjiao Wang,Zhixin Shu,Shiyang Cheng,Yannis Panagakis,Yannis Panagakis,Dimitris Samaras,Stefanos Zafeiriou +6 more
TL;DR: In this paper, a pseudo-supervised deep learning method was proposed to disentangle multiple latent factors of variation in face images captured in the wild by means of multilinear structure.
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Reconstruction for Feature Disentanglement in Pose-invariant Face Recognition
TL;DR: This paper presents a method for learning a feature representation that is invariant to pose, without requiring extensive pose coverage in training data, and proposes a Siamese network to explicitly disentangle identity and pose.
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