对比多个模型发现表情识别系统存在数据差异与情绪混淆问题。
Weaknesses of Facial Emotion Recognition Systems
- 选取三个主流模型与三组数据集进行跨数据集测试
- 发现不同数据集间表现差异大，特定情绪识别难度不一
- 尤其难以区分相似情绪，适合关注系统鲁棒性的研究者
从人脸中识别情绪是人机交互所需的重要机器学习任务。由于方法多样，本文深入分析相关研究，选取三种最具代表性的解决方案，并挑选出三组在图像数量和多样性上表现突出的数据集。对选定的神经网络模型进行训练后，开展一系列实验以比较其性能，包括在不同于训练数据集的测试集上验证。结果揭示现有方法存在明显弱点：不同数据集间性能差异显著，某些情绪识别更困难，且相近情绪（如愤怒与厌恶）之间难以准确区分。
原文摘要 · Abstract (English)
Emotion detection from faces is one of the machine learning problems needed for human-computer interaction. The variety of methods used is enormous, which motivated an in-depth review of articles and scientific studies. Three of the most interesting and best solutions are selected, followed by the selection of three datasets that stood out for the diversity and number of images in them. The selected neural networks are trained, and then a series of experiments are performed to compare their performance, including testing on different datasets than a model was trained on. This reveals weaknesses in existing solutions, including differences between datasets, unequal levels of difficulty in recognizing certain emotions and the challenges in differentiating between closely related emotions.
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