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 neurtal 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.