# A Survey of Deep Facial Attribute Analysis

arXiv: https://arxiv.org/abs/1812.10265 · HTML: https://mlsanity.com/paper/1812.10265/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning](https://mlsanity.com/paper/2302.11102/) (2023)
- [Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation](https://mlsanity.com/paper/2309.09667/) (2023)
- [Consistency and Accuracy of CelebA Attribute Values](https://mlsanity.com/paper/2210.07356/) (2022)
- [LogicNet: A Logical Consistency Embedded Face Attribute Learning Network](https://mlsanity.com/paper/2311.11208/) (2023)
- [CCR: Facial Image Editing with Continuity, Consistency and Reversibility](https://mlsanity.com/paper/2209.10734/) (2022)
- [Manipulating Vehicle 3D Shapes through Latent Space Editing](https://mlsanity.com/paper/2410.23931/) (2024)

## Abstract (arXiv)

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.