Global-local attention for emotion recognition
Global-local attention for emotion recognition
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DOI：
10.1007/s00521-021-06778-x
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发表时间：
2021-12-13
影响因子：
6
通讯作者：
Le, Bac
中科院分区：
文献类型：
--
作者：
Le, Nhat;Nguyen, Khanh;Le, Bac
人类情感识别是人工智能领域的一个活跃研究领域，在过去几年中取得了实质性进展。近年来的研究主要集中在人脸区域来推断人类情感，而周围的上下文信息没有得到有效利用。在本文中，我们提出了一种新的深度网络，使用一种新的全局-局部注意力机制来有效地识别人类情感。我们的网络旨在独立地从面部和上下文区域提取特征，然后使用注意力模块一起学习它们。通过这种方式，面部和上下文信息都被用来推断人类情感，从而增强分类器的区分度。密集的实验表明，我们的方法超过了目前的最先进的方法在最近的情感数据集的公平保证金。与以往的方法相比，我们的全局-局部注意力模块可以提取出更有意义的注意力地图。我们的网络的源代码和训练模型可在https://github.com/minhnhatvt/glamor-net上获得。
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.