Facing Imbalanced Data--Recommendations for the Use of Performance Metrics

  title={Facing Imbalanced Data--Recommendations for the Use of Performance Metrics},
  author={L{\'a}szl{\'o} A. Jeni and Jeffrey F. Cohn and Fernando De la Torre},
  journal={2013 Humaine Association Conference on Affective Computing and Intelligent Interaction},
Recognizing facial action units (AUs) is important for situation analysis and automated video annotation. Previous work has emphasized face tracking and registration and the choice of features classifiers. Relatively neglected is the effect of imbalanced data for action unit detection. While the machine learning community has become aware of the problem of skewed data for training classifiers, little attention has been paid to how skew may bias performance metrics. To address this question, we… CONTINUE READING
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