Basic emotion detection accuracy using artificial intelligence approaches in facial emotions recognition system: A systematic review

Abstract

Facial emotion recognition (FER) systems are pivotal in advancing human communication by interpreting emotions such as happiness, sadness, anger, fear, surprise, and disgust through artificial intelligence (AI). This systematic review examines the accuracy of detecting basic emotions, evaluates the features, algorithms, and datasets used in FER systems, and proposes a taxonomy for their integration into healthcare. A comprehensive search of six databases, covering publications from January 1990 to March 2023, identified 4073 articles, with 35 studies meeting inclusion criteria.

The review revealed that happiness and surprise achieved the highest mean detection accuracies (96.42 % and 96.32 %, respectively), whereas anger and disgust exhibited lower accuracies (91.68 % and 93.71 %, respectively). Fear and sadness had a mean accuracy of 93.87 %. Among AI algorithms, GFFNN demonstrated the highest accuracy (100 %), followed by KNN (97.99 %) and DDBNN (97.77 %). CNN and SVM were the most commonly used algorithms, showing competitive accuracies. The CK+ dataset, while extensively employed, demonstrated a mean accuracy of 96.08 %, lower than RAVDESS, Oulu-CASIA, and other databases.

This taxonomy provides insights into FER systems' capabilities to enhance patient care by identifying emotional states, pain levels, and overall well-being. Future research should adopt diverse datasets and advanced algorithms to improve FER accuracy, enabling robust integration of these systems into healthcare practices.