ERFN: Leveraging Context for Enhanced Emotion Detection

The majority of previous methods for identifying emotions concentrate on facial expressions rather than taking into account the rich contextual information that suggests significant emotional states. To fully utilize the contextual information in order to make up for the lack of emotion information. In this work, The Emotion Recognition Fusion Network (ERFN) is a novel model that uses advanced techniques for efficient context-aware identification of human emotion recognition. It incorporates the Flow Context Aware Loss Fusion (FCALF) model, which focuses on emotion analysis in a video sequence. The model uses deep feature extraction (VGG16), Farnebäck optical flow model, and L1 loss to calculate the Average Contextual Loss (ACL) for selecting key frames. The selected frames are used to obtain resultant optical flow images. Data augmentation techniques are applied exclusively to the training images. The resultant optical flow images undergo feature extraction using both InceptionResNetV2 and VGG16, fine-tuned by adding layer followed by GlobalMaxPool2D and a dense layer, capturing intricate details and flow-contextual information from face, body, and scene. The fused features are fed into a Softmax layer for classification. Experimental results show that the ERFN outperforms existing models in terms of accuracy and generalization, contributing to its effectiveness in capturing context-aware emotions. The proposed approach shows promising results in real-world uncontrolled environments (CAER-S) and laboratory-controlled (CK+) datasets.

@article{Gupta2024,
  title     = {ERFN: Leveraging Context for Enhanced Emotion Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Navneet Gupta and R. Vishnu Priya and Chandan Kumar Verma},
  doi       = {10.14569/IJACSA.2024.0150663},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150663}
}