Meet the Winners!


We are happy to announce the winners of Multimodal Emotion Recognition Challenge (MERC-2017)! The leaders were determined according to the score on the private test set (see Final Leaderboard):

  • First Place: tEarth (individual), score: 0,679511
  • Second Place10011000 (team of 2), score: 0.679001
  • Third Place: FedotovD (individual), score: 0.576905

We congratulate the winners and thank everyone who took part in the competition!

Register at Datacombats to stay informed on upcoming challenges.

Problem description

Most of the studies on emotion recognition problem are focused on single-channel recognition or multimodal approaches when the data is available for the whole dataset. However, in some practical cases data sources could be missed, noised or broken.

We presented the first machine learning competition on multimodal emotion recognition with missing data. The main goal of this challenge is to find approaches for a reliable recognition of emotional behavior when some data is unavailable.

The task consisted in predicting one of the six basic emotions (happiness, sadness, anger, disgust, fear and neutral state) based on the dataset of emotions acted by semi-professionals. Participants were presented with features for 4 modalities: audio, facial expressions, body-motion, and eye-tracking. They needed to beat the baseline solution based on naïve approach to compete for the prizes.

Competition Organizer and Sponsor — Neurodata Lab LLC, project company and R&D laboratory.


First place - $2000

Second place - $1000

Third place - $500


1. Registration:
October, 6th 2 p.m. UTC – November, 10th, 6 p.m. UTC

2. Competition Period (Submissions Accepted):
October, 18th 2 p.m. UTC – November, 13th, 3 a.m. UTC

3. Review & Winner Selection:
November, 13th – November, 28th

Competition Jury

Pavel Prihodko

Pavel Prihodko

PhD., Research Scientist, Center for Computational and Data-Intensive Science and Engineering, Skoltech

Alexey Potapov

Aleksey Potapov

D.Sc., Professor, Computer Photonics and Videomatics Department of ITMO University

Grigory Sterling

Grigoriy Sterling

Machine Learning Expert, Neurodata Lab LLC


Can you predict emotions on missing data with the same accuracy as on full data?

The solutions were judged by several criteria:

  • Accuracy of your predictions (public & private leaderboard metric)
  • Innovative approach
  • Applicability to other datasets
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