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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DeepStroke: An Efficient Stroke Screening Framework for Emergency Rooms with Multimodal Adversarial Deep Learning.
Tongan Cai,Haomiao Ni,Mingli Yu,Xiaolei Huang,Kelvin Wong,John Volpi,James Z. Wang,Stephen T. C. Wong +7 more
TL;DR: In this article, a multimodal deep learning framework, DeepStroke, is proposed to achieve computer-aided stroke presence assessment by recognizing the patterns of facial motion incoordination and speech inability for patients with suspicion of stroke in an acute setting.
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Robust Face Verification via Disentangled Representations.
TL;DR: A robust algorithm for face verification, i.e., deciding whether two images are of the same person or not, is introduced and has a higher clean and robust accuracy than state-of-the-art-methods when evaluated against white-box physical attacks.
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Open Source Face Recognition Performance Evaluation Package
Xiang Xu,Ioannis A. Kakadiaris +1 more
TL;DR: This work design and implement a light-weight, maintainable, scalable, generalizable, and extendable face recognition evaluation toolbox named FaRE that supports both online and offline evaluation to provide feedback to algorithm development and accelerate biometrics-related research.
Proceedings ArticleDOI
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction
TL;DR: Zhang et al. as mentioned in this paper proposed a novel UV map generation model that predicts the UV map from a single face image by selectively sampling the input face image's pixels and adjusting their relative locations, the UV sampler generates an incomplete UV map that could faithfully reconstruct the original face.
Proceedings ArticleDOI
Learning to Augment Face Presentation Attack Dataset via Disentangled Feature Learning from Limited Spoof Data
TL;DR: Wang et al. as mentioned in this paper propose to augment the limited data via disentangled feature learning, which includes the live/spoof classifi-cation task and the person identification task in a multi-task learning framework to disentangle the liveness and identity features.
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