深度面部表情识别基准测试：一项基于野外平衡数据集的扩展性评测协议
Benchmarking Deep Facial Expression Recognition: An Extensive Protocol with Balanced Dataset in the Wild
AI总结：
本研究构建了新的野外面部表情数据集，通过统一协议评测23种网络架构及多种训练设置的性能，给出真实场景部署深度FER方法的建议，并探讨相关伦理与隐私问题。
摘要：
面部表情识别（FER）是人机交互的关键组成部分。现有FER方法依托各类开源深度模型与训练方案，已实现较高的准确率与泛化能力。但这类方法在实际场景中的表现往往欠佳，而相关场景的研究目前仍较为匮乏。本文收集了一个全新的野外面部表情数据集，用于跨域验证。我们遵循统一协议，实现并评估了23种常用网络架构。此外，我们还验证了输入分辨率、类别平衡处理、预训练策略等多种设置对性能的相应贡献。基于在三个大规模FER数据集上开展的大量实验，以及我们的实际跨验证工作，我们对网络架构进行了排名，并总结了一套在真实场景中部署深度FER方法的建议。此外，本文还探讨了营销、教育、娱乐行业等实际FER应用中潜在的伦理准则、隐私问题与监管规范。
英文摘要：
Facial expression recognition (FER) is a crucial part of human-computer interaction. Existing FER methods achieve high accuracy and generalization based on different open-source deep models and training approaches. However, the performance of these methods is not always good when encountering practical settings, which are seldom explored. In this paper, we collected a new in-the-wild facial expression dataset for cross-domain validation. Twenty-three commonly used network architectures were implemented and evaluated following a uniform protocol. Moreover, various setups, in terms of input resolutions, class balance management, and pre-trained strategies, were verified to show the corresponding performance contribution. Based on extensive experiments on three large-scale FER datasets and our practical cross-validation, we ranked network architectures and summarized a set of recommendations on deploying deep FER methods in real scenarios. In addition, potential ethical rules, privacy issues, and regulations were discussed in practical FER applications such as marketing, education, and entertainment business.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
