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
Sampling Strategies for GAN Synthetic Data
TL;DR: This work proposes to maximally utilise the parameters learned during training of the GAN itself, including discriminator’s realism confidence score and the confidence on the target label of the synthetic data to train deep CNNs more efficiently and accurately.
Journal ArticleDOI
Self-attention driven adversarial similarity learning network
TL;DR: A self-attention driven adversarial similarity learning network that forces the topic vectors to not only assign discriminative similarity scores to different object pairs but also further mine the hidden semantic information within data distribution.
Proceedings ArticleDOI
Generative Adversarial Image Synthesis with Decision Tree Latent Controller
TL;DR: The decision tree latent controller generative adversarial network (DTLC-GAN), an extension of a GAN that can learn hierarchically interpretable representations without relying on detailed supervision, is proposed and evaluated on various datasets and showed its effectiveness in representation learning.
Journal ArticleDOI
Joint Deep Learning of Facial Expression Synthesis and Recognition
TL;DR: A novel joint deep learning of facial expression synthesis and recognition method for effective FER and an intra- class loss with a novel real data-guided back-propagation (RDBP) algorithm to reduce the intra-class variations of images from the same class, which can significantly improve the final performance.
Book ChapterDOI
Towards Learning a Realistic Rendering of Human Behavior
TL;DR: This work presents an approach towards a holistic learning framework for rendering human behavior in which all components are learned from easily available data and shows a new path towards easily available, personalized avatar creation.
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