Proceedings ArticleDOI
Towards Pose Invariant Face Recognition in the Wild
Jian Zhao,Yu Cheng,Yan Xu,Lin Xiong,Jianshu Li,Fang Zhao,Karlekar Jayashree,Sugiri Pranata,Sheng Mei Shen,Junliang Xing,Shuicheng Yan,Jiashi Feng +11 more
- pp 2207-2216
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TLDR
Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of the proposed Pose Invariant Model for face recognition in the wild over the state of thearts.Abstract:
Pose variation is one key challenge in face recognition. As opposed to current techniques for pose invariant face recognition, which either directly extract pose invariant features for recognition, or first normalize profile face images to frontal pose before feature extraction, we argue that it is more desirable to perform both tasks jointly to allow them to benefit from each other. To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties. First, PIM is a novel and unified deep architecture, containing a Face Frontalization sub-Net (FFN) and a Discriminative Learning sub-Net (DLN), which are jointly learned from end to end. Second, FFN is a well-designed dual-path Generative Adversarial Network (GAN) which simultaneously perceives global structures and local details, incorporated with an unsupervised cross-domain adversarial training and a "learning to learn" strategy for high-fidelity and identity-preserving frontal view synthesis. Third, DLN is a generic Convolutional Neural Network (CNN) for face recognition with our enforced cross-entropy optimization strategy for learning discriminative yet generalized feature representation. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of the proposed model over the state-of-the-arts.read more
Citations
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Journal ArticleDOI
Deep face recognition: A survey
Mei Wang,Weihong Deng +1 more
TL;DR: A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.
Journal ArticleDOI
A survey on deep learning based face recognition
Guodong Guo,Guodong Guo,Na Zhang +2 more
TL;DR: Major deep learning concepts pertinent to face image analysis and face recognition are reviewed, and a concise overview of studies on specific face recognition problems is provided, such as handling variations in pose, age, illumination, expression, and heterogeneous face matching.
Proceedings ArticleDOI
Disentangled and Controllable Face Image Generation via 3D Imitative-Contrastive Learning
TL;DR: Li et al. as mentioned in this paper proposed an approach for face image generation of virtual people with disentangled, precisely-controllable latent representations for identity of non-existing people, expression, pose, and illumination.
Book ChapterDOI
Instance Adaptive Self-training for Unsupervised Domain Adaptation
TL;DR: To effectively improve the quality of pseudo-labels, a novel pseudo-label generation strategy with an instance adaptive selector is developed and the region-guided regularization to smooth the pseudo- label region and sharpen the non-pseudo-label region is proposed.
Proceedings ArticleDOI
Unsupervised Face Normalization With Extreme Pose and Expression in the Wild
TL;DR: This work proposes a Face Normalization Model (FNM) to generate a frontal, neutral expression, photorealistic face image for face recognition, and presents a series of face attention discriminators to refine local textures.
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