Multiview Facial Expression Recognition (MFER) is a well-known interdisciplinary problem among computer science and related disciplines with promising and valuable applications. Recognizing the facial expression in pose variations, which is very common in real-world conditions, makes it very challenging. This paper aims to provide a comprehensive survey of the MFER progress, includingboth categories of traditional and deep approaches. In general, we sort each of these categories into three overall groups to meet the pose variations: Pose-Robust Features, Pose Normalization, and Pose-Specific Classification. While reviewing the traditional methods, a thorough study is proposed on the existing novel deep techniques. We also introduce the state-of-the-art and discuss the challenges, limitations, opportunities, and future trends that need to be addressed in this field. Moreover, we provide an extensive review of publicly available datasets for MFER, including the labs’ collections and the sets gathered from in the wild. Besides, we introduce the most popular protocols on each dataset to standardize comparisons in the future.

中文翻译：

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多视图面部表情识别调查


多视图面部表情识别（MFER）是计算机科学及相关学科中众所周知的跨学科问题，具有广阔的应用前景和价值。识别姿势变化中的面部表情在现实条件下非常常见，因此非常具有挑战性。本文旨在对 MFER 进展进行全面调查，包括传统方法和深度方法。一般来说，我们将每个类别分为三组以满足姿势变化：姿势鲁棒特征、姿势标准化和姿势特定分类。在回顾传统方法的同时，提出对现有的新颖的深层技术进行深入研究。我们还介绍了最新技术，并讨论了该领域需要解决的挑战、限制、机遇和未来趋势。此外，我们还对 MFER 的公开数据集进行了广泛的审查，包括实验室收集的数据集和从野外收集的数据集。此外，我们在每个数据集上引入了最流行的协议，以便将来标准化比较。