A Survey of Deep Facial Attribute Analysis

TL;DR

This survey comprehensively reviews deep learning techniques for facial attribute analysis, covering estimation and manipulation, datasets, methods, applications, challenges, and future directions.

Contribution

It provides a detailed taxonomy and analysis of state-of-the-art deep facial attribute estimation and manipulation methods, integrating theories, datasets, and applications.

Findings

Summarizes key datasets and metrics used in the field.

Classifies and reviews recent deep learning algorithms for FAE and FAM.

Discusses challenges and future research directions.

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.