Emotion Recognition in Real-world Conditions with Acoustic and Visual Features

Abstract

There is an enormous number of potential applications of the system which is capable to recognize human emotions. Such opportunity can be useful in various applications, e.g., improvement of Spoken Dialogue Systems (SDSs) or monitoring agents in call-centers. Therefore, the Emotion Recognition In The Wild Challenge 2014 (EmotiW 2014) is focused on estimating emotions in real-world situations. This study presents the results of multimodal emotion recognition based on support vector classifier. The described approach results in 41.77% of overall classification accuracy in the multimodal case. The obtained result is more than 17% higher than the baseline result for multimodal approach.