@inproceedings{dente2017measures,
  title={Measures and metrics for automatic emotion classification via FACET},
  abstract = {For dynamic emotions to be modelled in a natural and convincing way, systems must rely on accurate affective analysis of facial expressions in the first place. The present work introduces two measures for evaluating automatic emotion classification performance. It further provides a systematic comparison between 14 databases of dynamic expressions. Machine analysis was conducted using the FACET system, with an algorithm calculating recognition sensitivity and confidence. Results revealed the proportion of facial stimuli that could be recognised by the machine algorithm above threshold evidence, showing significant differences in recognition performance between the databases.},
  author={Dente, Pasquale and K{\"u}ster, Dennis and Skora, Lina and Krumhuber, E},
  url = {https://www.csl.uni-bremen.de/cms/images/documents/publications/Dente_et_al_2017_Measures_and_metrics_for_automatic_emotion_classif.pdf},
  booktitle={Proceedings of the Conference on the Study of Artificial Intelligence and Simulation of Behaviour (AISB)},
  pages={160--163},
  year={2017}
}