Most of 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.
Here we present you with 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.
Your task will be to predict one of the six basic emotions (happiness, sadness, anger, disgust, fear and neutral state) based on the dataset of emotions acted by semi-professionals. You will be presented with features for 4 modalities: audio, facial expressions, body-motion and eye-tracking. You need 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
The winners will be determined by the leaderboard ranking based on private test set.
Special prizes might be awarded at the discretion of the Jury.
Meet the Jury
Jump on board
Can you predict emotions on missing data with the same accuracy as on full data?
Your solutions will be judged by several criteria:
- Accuracy of your predictions (public & private leaderboard metric)
- Innovative approach
- Applicability to other datasets
Full problem description and data will be available upon the official start of the challenge. Register to get the access.
Team participation is allowed. Register individually to form your team later.
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