Emotion Recognition as a tool to support personalized therapies
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1266Keywords:
Hybrid Architectures, Recognition of Emotions in Facial Expressions, Personalized TherapiesAbstract
Background: In therapeutic contexts, emotion recognition systems can be a valuable tool for patients with emotional expression difficulties. Objective: Therefore, this work aims to present a comparison between hybrid architectures to perform emotion recognition in facial expressions. Method: The proposed architectures were trained-validated with the FER2013 database and are based on Wavelet decomposition and Transfer Learning. Different data preprocessing configurations were also explored. Result: As a result, the architecture composed of a VGG16 and a Random Forest obtained 74.52% accuracy in training and 84.72% in testing, with only 27% of the attributes of VGG16. The DWNN architecture, with 4 layers and Random Forest, achieved 70.77% accuracy in training and 81.21% in testing, using 34% of the attributes. Conclusion: The best architecture will compose an emotion recognition system for personalizing therapies.
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