Explainable gradient convolutional vector fuzzy pattern analysis based on ensemble model for facial expression recognition.

Facial expression recognition using machine learning involves training algorithms to identify and categorize human emotions based on visual cues from facial features. Explainable AI (XAI) enhances this process by providing transparency into how these algorithms arrive at their predictions. While machine learning algorithms provide the capability to recognize facial expressions, explainable AI offers the crucial ability to understand and interpret these recognition processes, leading to more robust, fair, and trustworthy systems. The aim of this research is to propose a novel method in facial expression recognition using segmentation by an ensemble machine learning algorithm and explainable AI model. The input consists of facial expression images, which are first processed for noise removal and normalization. The processed images are then segmented using the Explainable Gradient Convolutional Vector Fuzzy Pattern Recognition (ExGrConVFuzPR) model. The proposed method was evaluated on the JAFFE, CK, and AFLW datasets. The model achieved promising results with an accuracy of 97%, precision of 96%, recall of 96%, F1-score of 97%, and RMSE of 0.043. These outcomes demonstrate that the suggested approach provides good performance along with improved interpretability.