Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News
<|reference_start|>Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News: 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<|reference_end|>
@article{zhang2024authentic,
    title={Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News},
    author={Qixuan Zhang, Zhifeng Wang, Yang Liu, Zhenyue Qin, Kaihao Zhang,
  Sabrina Caldwell, Tom Gedeon},
    journal={arXiv preprint arXiv:2404.13493},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13493},
    primaryClass={cs.CV}
}