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

TL;DR

This paper investigates emotion features and fusion strategies for audio-visual emotion recognition, achieving competitive results by exploring various features and fusion methods in the EmotiW 2019 challenge.

Contribution

It introduces novel combinations of audio and visual features with advanced fusion strategies, including attention mechanisms and bilinear pooling, for improved emotion recognition.

Findings

Achieved 65.5% accuracy on AFEW validation set

Achieved 62.48% accuracy on test set

Ranked third in EmotiW 2019 challenge

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.