BEE-NET: A deep neural network to identify in-the-wild Bodily Expression of Emotions
<|reference_start|>BEE-NET: A deep neural network to identify in-the-wild Bodily Expression of Emotions: In this study, we investigate how environmental factors, specifically the scenes and objects involved, can affect the expression of emotions through body language. To this end, we introduce a novel multi-stream deep convolutional neural network named BEE-NET. We also propose a new late fusion strategy that incorporates meta-information on places and objects as prior knowledge in the learning process. Our proposed probabilistic pooling model leverages this information to generate a joint probability distribution of both available and anticipated non-available contextual information in latent space. Importantly, our fusion strategy is differentiable, allowing for end-to-end training and capturing of hidden associations among data points without requiring further post-processing or regularisation. To evaluate our deep model, we use the Body Language Database (BoLD), which is currently the largest available database for the Automatic Identification of the in-the-wild Bodily Expression of Emotions (AIBEE). Our experimental results demonstrate that our proposed approach surpasses the current state-of-the-art in AIBEE by a margin of 2.07%, achieving an Emotional Recognition Score of 66.33%.<|reference_end|>
@article{dehshibi2024bee-net:,
    title={BEE-NET: A deep neural network to identify in-the-wild Bodily Expression
  of Emotions},
    author={Mohammad Mahdi Dehshibi and David Masip},
    journal={arXiv preprint arXiv:2402.13955},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13955},
    primaryClass={cs.CV}
}