Optimization-based clustering feature extraction approach for human emotion recognition

Human emotions are mental health states that resolve without conscious effort and are followed by physiological effects in the face muscles that represent expressions. In many applications of human-computer interaction, nonverbal communication mechanisms such as emotions, eye movements, and motions are used. Since there is no contrast among the emotions of a face and there is also a lot of variety and complexity, identifying emotions is a difficult process. To model the face, the machine learning system leverages some open features. Automatic emotion recognition based on face expression is a fascinating study area that has been presented and utilized in a variety of fields, including safety, health, and human-machine interactions. Researchers in this subject are willing to develop strategies to understand, code, and extract facial expressions in order to improve computer prediction. Machine learning, being one of the most promising new fields, offers a wide range of applications. In recent years, the support vector clustering technique has gotten a lot of attention. In this research paper, the use of ant colony optimization (ACO) for creating k-cluster planes and assigning each data sample to the correct cluster is proposed in this study as an upgraded clustering approach. SVC is used in this improved technique to refine the clusters created by ACO. The human face expressions are segmented using this upgraded clustering method. The suggested clustering technique is compared to an existing segmentation approach for emotion recognition using a variety of criteria.