A Survey of Deep Facial Attribute Analysis
arXiv:1812.10265
Abstract
Facial attribute analysis has received considerable attention when deep learning techniques made remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis consists of two basic sub-issues: facial attribute estimation (FAE), which recognizes whether facial attributes are present in given images, and facial attribute manipulation (FAM), which synthesizes or removes desired facial attributes. In this paper, we provide a comprehensive survey of deep facial attribute analysis from the perspectives of both estimation and manipulation. First, we summarize a general pipeline that deep facial attribute analysis follows, which comprises two stages: data preprocessing and model construction. Additionally, we introduce the underlying theories of this two-stage pipeline for both FAE and FAM. Second, the datasets and performance metrics commonly used in facial attribute analysis are presented. Third, we create a taxonomy of state-of-the-art methods and review deep FAE and FAM algorithms in detail. Furthermore, several additional facial attribute related issues are introduced, as well as relevant real-world applications. Finally, we discuss possible challenges and promising future research directions.
arXiv commentsubmitted to International Journal of Computer Vision (IJCV)
References in corpus (20)
- Conditional Generative Adversarial Nets
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Deep Learning Face Representation by Joint Identification-Verification
- Tutorial on Variational Autoencoders
- Dual Learning for Machine Translation
- Invertible Conditional GANs for image editing
- Facial Landmark Detection: a Literature Survey
- IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
- Deep Identity-aware Transfer of Facial Attributes
- Self-supervised learning of a facial attribute embedding from video
- Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification
- Facial Attributes: Accuracy and Adversarial Robustness
- DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images
- Mask-aware Photorealistic Face Attribute Manipulation
- A Deep Cascade Network for Unaligned Face Attribute Classification
- Load Balanced GANs for Multi-view Face Image Synthesis
- Hierarchical Adversarially Learned Inference
- A Jointly Learned Deep Architecture for Facial Attribute Analysis and Face Detection in the Wild
- Variational Capsules for Image Analysis and Synthesis
- XOGAN: One-to-Many Unsupervised Image-to-Image Translation