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ABSTRACT
We analyze the results of the 2017 ChaLearn Looking at People Challenge at ICCV The challenge comprised three tracks: (1) large-scale isolated (2) continuous gesture recognition, and (3) real versus fake expressed emotions tracks. It is the second round for both gesture recognition challenges, which were held first in the context of the ICPR 2016 workshop on "multimedia challenges beyond visual analysis". In this second round, more participants joined the competitions, and the performances considerably improved compared to the first round. Particularly, the best recognition accuracy of isolated gesture recognition has improved from 56.90% to 67.71% in the IsoGD test set, and Mean Jaccard Index (MJI) of continuous gesture recognition has improved from 0.2869 to 0.6103 in the ConGD test set. The third track is the first challenge on real versus fake expressed emotion classification, including six emotion categories, for which a novel database was introduced. The first place was shared between two teams who achieved 67.70% averaged recognition rate on the test set. The data of the three tracks, the participants' code and method descriptions are publicly available to allow researchers to keep making progress in the field.
INDEX TERMS
Gesture recognition, Feature extraction, Emotion recognition, Skeleton, Spatiotemporal phenomena, Training
CITATION

J. Wan et al., "Results and Analysis of ChaLearn LAP Multi-modal Isolated and Continuous Gesture Recognition, and Real Versus Fake Expressed Emotions Challenges," 2017 IEEE International Conference on Computer Vision Workshop (ICCVW), Venice, Italy, 2017, pp. 3189-3197.
doi:10.1109/ICCVW.2017.377
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