Smart Facial Expression Analysis: Fuzzy Extreme Learning Machine in Emotion Detection

The immense academic and economic potential of facial emotion recognition (FER) has made it a crucial field in computer vision and artificial intelligence. Because of the fundamental role that facial expressions play in interpersonal communication; face photographs are vital for analysing human emotions within the context of Smart Facial Expression Analysis. This research provides a successful pipeline for emotion identification and examines FER methods that rely just on face pictures. Preprocessing, segmentation, feature extraction, and training the model are the steps that make up the suggested method's organised procedure. Face detection using the Viola-Jones technique is the first step in the preprocessing phase. Four rectangular characteristics are used for segmentation, with greyscale conversion being a necessity. In order to train a fuzzy -ELM model, feature extraction uses HOS and LBP. Emotions are better understood with this method. The suggested fuzzy-ELM approach outperforms two state-of-the-art models, ELM and CNN. With an accuracy of 98.33 %, the experimental findings show a substantial improvement in precision. A dependable and high-performing method for emotion recognition using just facial imaging, these findings highlight the usefulness of the suggested approach for Smart Facial Expression Analysis.