Creative Guidance of Intelligent Emotion Recognition in Video Art

This study optimizes the existing emotion recognition model to improve the application effect of emotion recognition technology in the guidance of video art creation. It also compares the performances of Multimodal Sentiment Analysis (MMSA), Multimodal Sequence Encoder (MuSE), and the optimized model through a series of simulation experiments. In the fine-grained accuracy of emotion recognition, the optimized model performs well in micro-expression recognition accuracy and multimodal fusion effect, and scores 4.17 and 4.96, respectively. In the robustness test under a complex background, the background interference resistance score of the optimized model is 4.30, and the modal mismatch processing score is 4.38. In the experiment on the effectiveness of emotion recognition in creative guidance, the optimized model scores 4.40 and 3.66 in terms of artistic expression enhancement and creative feedback accuracy, respectively. The experimental results show that the optimized model is superior to the existing models in several key dimensions of emotion recognition, especially in enhancing the emotional expression of artistic works and providing creative guidance. Therefore, this study provides some reference for the application and development of emotion recognition technology in video art creation.

@article{Chen2025,
  title     = {Creative Guidance of Intelligent Emotion Recognition in Video Art},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Weixing Chen and Yubo Zhou},
  doi       = {10.14569/IJACSA.2025.0161213},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161213}
}