Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition

Abstract

The audio-video based emotion recognition aims to classify a given video into basic emotions. In this paper, we describe our approaches in EmotiW 2019, which mainly explores emotion features and feature fusion strategies for audio and visual modality. For emotion features, we explore audio feature with both speech-spectrogram and Log Mel-spectrogram and evaluate several facial features with different CNN models and different emotion pretrained strategies. For fusion strategies, we explore intra-modal and cross-modal fusion methods, such as designing attention mechanisms to highlights important emotion feature, exploring feature concatenation and factorized bilinear pooling (FBP) for cross-modal feature fusion. With careful evaluation, we obtain 65.5% on the AFEW validation set and 62.48% on the test set and rank third in the challenge.

Accepted by ACM ICMI'19 (2019 International Conference on Multimodal Interaction)

References in corpus (1)

• Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition

Cited by in corpus (5)

• A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition
• SZTU-CMU at MER2024: Improving Emotion-LLaMA with Conv-Attention for Multimodal Emotion Recognition
• AU-Guided Unsupervised Domain Adaptive Facial Expression Recognition
• Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023
• Information Fusion in Attention Networks Using Adaptive and Multi-level Factorized Bilinear Pooling for Audio-visual Emotion Recognition