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Real world expression recognition: A highly imbalanced detection problem

机译：真实世界的表情识别：一个高度不平衡的检测问题

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State-of-the-art methods have reported very high performance on facial expression detection. However, nearly all these previous work was conducted in strictly controlled environment, what's more, effects of imbalanced data have been neglected. A new database, RAF-DB, is constructed to provide abundant images with expression labels from different people in different real-world conditions. Annotation result suggests that emotion in real world presents strongly imbalanced distribution. To address this problem, we conducted experiments on RAF-DB using several proposed imbalanced learning methods. A new face-aiming methods VFSG also has been put forward to perform well among over-sampling methods. Besides, we explored some other complications of the imbalanced expression detection task, imbalance ratio, expression characteristics and performance metrics. Our findings suggest that imbalanced learning strategies are indispensable for detecting rare expressions, and real-world expression database should be used which can reflect closely the authentic expression status in daily life.
机译：最先进的方法在面部表情检测上报道了非常高的性能。然而，几乎所有这些以前的工作都在严格控制的环境中进行了，更重要的是，不平衡数据的影响被忽略了。建立一个新的数据库RAF-DB，以提供来自不同人物中不同人的表达标签提供丰富的图像。注释结果表明，现实世界中的情绪呈现出强烈的分布。为了解决这个问题，我们使用几种提出的不平衡学习方法对RAF-DB进行了实验。一个新的瞄准方法VFSG也已经提出，在过采样方法中表现出色。此外，我们探讨了不平衡表达检测任务，不平衡率，表达特征和性能指标的其他一些并发症。我们的研究结果表明，用于检测稀有表达式的不平衡学习策略是必不可少的，并且应该使用实际表达式数据库，这可以密切地反映日常生活中的真实表达状态。

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