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
MDKE: Multi-level Disentangled Knowledge-Based Embedding for Recommender Systems
TL;DR: Zhang et al. as mentioned in this paper divide the embedding learning of users and items into disentangled semantic-level and structural-level subspaces, and propose a graph embedding-based module to capture local structure features on the redefined knowledge interaction graph.
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Enhancing Low-resolution Face Recognition with Feature Similarity Knowledge Distillation
TL;DR: Li et al. as discussed by the authors proposed a feature knowledge distillation framework to improve low-resolution face recognition performance using knowledge obtained from high-resolution (HR) images, which achieved a 3% improvement over the previous state-of-the-art method on the AgeDB-30 benchmark without bells and whistles.
References
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Posted Content
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