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. Author manuscript; available in PMC: 2020 Jun 18.
Published in final edited form as: IEEE Trans Games. 2018 Oct 22;12(2):213–218. doi: 10.1109/tg.2018.2877325

Fig. 4:

Fig. 4:

A comparison of Azure Emotion API’s performance with our max-reduction and aggregation techniques on a per-class basis shows that our algorithm can correctly label the majority of frames in every category except scared and angry, and our aggregation-based method outperforms the best of the three commercial emotion recognition APIs in every category.