Authentic Emotion Mapping: Benchmarking Facial Expressions in Real News
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Abstract (arXiv)
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
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 35 references that are in the map. First posted on arXiv 21 Apr 2024.
Builds on
12 of 35 references.
References · 12
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- 2019Exploiting multi-CNN features in CNN-RNN based Dimensional Emotion Recognition on the OMG in-the-wild Datasetcited by 39
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- 2021Graph-based Facial Affect Analysis: A Reviewcited by 14
- 2020Generative Adversarial Networks in Human Emotion Synthesis:A Reviewcited by 4
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Citing papers per year: 2024: 1.
1 paper.