  Multimodal emotion analysis performed better in emotion recognition depending
on more comprehensive emotional clues and multimodal emotion dataset. In this
paper, we developed a large multimodal emotion dataset, named "HED" dataset, to
facilitate the emotion recognition task, and accordingly propose a multimodal
emotion recognition method. To promote recognition accuracy, "Feature After
Feature" framework was used to explore crucial emotional information from the
aligned face, body and text samples. We employ various benchmarks to evaluate
the "HED" dataset and compare the performance with our method. The results show
that the five classification accuracy of the proposed multimodal fusion method
is about 83.75%, and the performance is improved by 1.83%, 9.38%, and 21.62%
respectively compared with that of individual modalities. The complementarity
between each channel is effectively used to improve the performance of emotion
recognition. We had also established a multimodal online emotion prediction
platform, aiming to provide free emotion prediction to more users.
