真实情感映射：真实新闻中面部表情的基准测试
Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News
AI总结：
本文提出基于真实新闻视频面部关键点的情感识别新基准，利用图神经网络分析关键点几何关系，提供比RGB图像更高效的替代方案，为情感识别研究指明新方向。
摘要：
本文提出了一个情感识别的新基准，使用从真实新闻视频中提取的面部关键点。依赖RGB图像的传统方法资源消耗大，而我们基于面部关键点情感识别（FLER）的方法提供了一个简化而有效的替代方案。通过利用图神经网络（GNNs）分析面部关键点的几何与空间关系，该方法提升了情感识别的理解与准确率。我们探讨了深度学习技术在情感识别中的进展与挑战，特别关注图神经网络（GNNs）和Transformers。实验结果证明了我们数据集作为基准的可行性与潜力，为未来情感识别技术研究设定了新方向。代码与模型见：https://github.com/wangzhifengharrison/benchmark_real_news
英文摘要：
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: https://github.com/wangzhifengharrison/benchmark_real_news
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
