Global-Local Attention for Emotion Recognition
This addresses emotion recognition for AI applications by improving accuracy through better use of context, but it is incremental as it builds on existing attention-based methods.
The paper tackled emotion recognition by proposing a global-local attention mechanism to utilize both facial and context information, resulting in surpassing state-of-the-art methods on recent datasets by a fair margin.
Human emotion recognition is an active research area in artificial intelligence and has made substantial progress over the past few years. Many recent works mainly focus on facial regions to infer human affection, while the surrounding context information is not effectively utilized. In this paper, we proposed a new deep network to effectively recognize human emotions using a novel global-local attention mechanism. Our network is designed to extract features from both facial and context regions independently, then learn them together using the attention module. In this way, both the facial and contextual information is used to infer human emotions, therefore enhancing the discrimination of the classifier. The intensive experiments show that our method surpasses the current state-of-the-art methods on recent emotion datasets by a fair margin. Qualitatively, our global-local attention module can extract more meaningful attention maps than previous methods. The source code and trained model of our network are available at https://github.com/minhnhatvt/glamor-net