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.

中文翻译：

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深度人脸属性分析综述

过去几年，当深度学习技术在该领域取得显着突破时，面部属性分析受到了相当大的关注。基于深度学习的面部属性分析由两个基本子问题组成：面部属性估计 (FAE)，它识别给定图像中是否存在面部属性，以及面部属性操作 (FAM)，它合成或删除所需的面部属性。在本文中，我们从估计和操作的角度对深度面部属性分析进行了全面调查。首先，我们总结了深度面部属性分析遵循的一般管道，它包括两个阶段：数据预处理和模型构建。此外，我们还介绍了 FAE 和 FAM 两阶段管道的基础理论。第二，介绍了面部属性分析中常用的数据集和性能指标。第三，我们创建了最先进方法的分类，并详细审查了深度 FAE 和 FAM 算法。此外，还介绍了几个额外的面部属性相关问题，以及相关的实际应用。最后，我们讨论了可能的挑战和有前途的未来研究方向。