Audio Visual Emotion Recognition with Temporal Alignment and Perception Attention
This work addresses emotion recognition from video for applications like human-computer interaction, but it is incremental as it builds on existing LSTM and attention methods.
The paper tackled audio-visual emotion recognition by addressing temporal alignment and perception attention, achieving improved efficiency on the EmotiW2015 dataset.
This paper focuses on two key problems for audio-visual emotion recognition in the video. One is the audio and visual streams temporal alignment for feature level fusion. The other one is locating and re-weighting the perception attentions in the whole audio-visual stream for better recognition. The Long Short Term Memory Recurrent Neural Network (LSTM-RNN) is employed as the main classification architecture. Firstly, soft attention mechanism aligns the audio and visual streams. Secondly, seven emotion embedding vectors, which are corresponding to each classification emotion type, are added to locate the perception attentions. The locating and re-weighting process is also based on the soft attention mechanism. The experiment results on EmotiW2015 dataset and the qualitative analysis show the efficiency of the proposed two techniques.