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首页> 外文会议>2016 International Conference on Biometrics >Real world expression recognition: A highly imbalanced detection problem
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Real world expression recognition: A highly imbalanced detection problem

机译：现实世界中的表情识别：高度不平衡的检测问题

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摘要

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方面已报告了很高的性能。但是，几乎所有这些先前的工作都是在严格受控的环境中进行的，而且，不平衡数据的影响已被忽略。构建了一个新数据库RAF-DB，以提供来自不同现实条件下来自不同人群的表达标签的丰富图像。注释结果表明，现实世界中的情绪呈现出严重的不平衡分布。为了解决这个问题，我们使用几种建议的不平衡学习方法对RAF-DB进行了实验。还提出了一种新的面部对准方法VFSG，以在过采样方法中表现良好。此外，我们还探讨了表达失衡检测任务，失衡比，表达特征和性能指标的其他一些复杂问题。我们的发现表明，不平衡的学习策略对于检测稀有表达是必不可少的，应该使用真实世界的表达数据库，该数据库可以紧密反映日常生活中的真实表达状态。

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