  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
