Abstract
In this paper, we present a novel benchmark for Emotion Recognition using facial landmarks extracted from realistic news videos. Traditional methods relying on RGB images are resource-intensive, whereas our approach with Facial Landmark Emotion Recognition (FLER) offers a simplified yet effective alternative. By leveraging Graph Neural Networks (GNNs) to analyze the geometric and spatial relationships of facial landmarks, our method enhances the understanding and accuracy of emotion recognition. We discuss the advancements and challenges in deep learning techniques for emotion recognition, particularly focusing on Graph Neural Networks (GNNs) and Transformers. Our experimental results demonstrate the viability and potential of our dataset as a benchmark, setting a new direction for future research in emotion recognition technologies. The codes and models are at: this https URL
Abstract (translated)
在本文中，我们提出了一个用于情感识别的基准，该基准是基于从现实新闻视频中提取的人脸特征点。传统依赖RGB图像的方法 resource-intensive，而我们的方法 Facial Landmark Emotion Recognition (FLER) 提供了一种简化的且有效的替代方案。通过利用图神经网络（GNNs）分析人脸特征点的几何和空间关系，我们的方法提高了情感识别的理解和准确性。我们讨论了用于情感识别的深度学习技术的进步和挑战，特别是关注图神经网络（GNNs）和Transformer。我们的实验结果表明，我们的数据集作为基准是可行的，为未来情感识别技术的研究奠定了新的方向。代码和模型在此处：<https:// this URL>
URL
https://arxiv.org/abs/2404.13493
