Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild
This work addresses the challenge of recognizing human emotions in close-to-real-world settings for applications in human-computer interaction, representing an incremental advancement in multimodal fusion techniques.
The paper tackled emotion recognition in real-world environments by proposing feature-level and model-level fusion strategies for audio and visual modalities, achieving significant improvements over baseline methods and comparable or better performance than state-of-the-art methods on the EmotiW2018 AFEW dataset, with model-level fusion performing better when one modality fails.
Emotion recognition plays an important role in human-computer interaction (HCI) and has been extensively studied for decades. Although tremendous improvements have been achieved for posed expressions, recognizing human emotions in "close-to-real-world" environments remains a challenge. In this paper, we proposed two strategies to fuse information extracted from different modalities, i.e., audio and visual. Specifically, we utilized LBP-TOP, an ensemble of CNNs, and a bi-directional LSTM (BLSTM) to extract features from the visual channel, and the OpenSmile toolkit to extract features from the audio channel. Two kinds of fusion methods, i,e., feature-level fusion and model-level fusion, were developed to utilize the information extracted from the two channels. Experimental results on the EmotiW2018 AFEW dataset have shown that the proposed fusion methods outperform the baseline methods significantly and achieve better or at least comparable performance compared with the state-of-the-art methods, where the model-level fusion performs better when one of the channels totally fails.