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Customer satisfaction can be measured using facial expression recognition. The current generation of artificial intelligence systems heavily depends on facial features such as eyebrows, eyes, and foreheads. This dependence introduces a limitation as people generally prefer to conceal their genuine emotions. As body gestures are difficult to conceal and can convey a more detailed and accurate emotional state, the authors incorporate upper-body gestures as an additional feature that improves the predicted emotion’s accuracy. This work uses an ensemble machine-learning model that integrates support vector machines, random forest classifiers, and logistic regression classifiers. The proposed method detects emotions from facial expressions and upper-body movements and is experimentally evaluated and has been found to be effective, with an accuracy rate of 97% on the EMOTIC dataset and 99% accuracy on MELD dataset.

Bi-Model Engagement Emotion Recognition Based on Facial and Upper-Body Landmarks and Machine Learning Approaches