JoWUA

Abstract

Introduction: Facial Emotion Recognition (FER) is vital to the study of human emotion, as it enables the analysis of facial expressions. Nevertheless, current FER techniques do not effectively identify emotions in people with facial wrinkles, which are common in older people, leading to incorrect classifications. This research aims to recommend a superior FER model that considers facial wrinkles as a way of improving the identification of emotions and the degree of emotion. Methodology: The hybrid methodology is introduced. Processing of input photos begins with noise removal and contrast enhancement. Facial features are then derived using the Logarithmic Function-centric Viola-Jones (LF-VJ) algorithm, which detects faces and segments facial objects. The wrinkle score and cosine similarity are computed to measure the existence of wrinkles. Entropy Weighted Secretary Bird Optimization (EWSBO) is used to do optimal feature selection and then RSRN is used to classify emotions. STFIS has to do intensity estimation to assess the intensity of identified emotions. Results: The accuracy of the proposed framework on emotion classification was 98.97 and precision, recall and F-measure values were beyond 99. The estimation of the intensity was correct and rule generation, fuzzification and defuzzification time were 958.697ms, 923.013ms and 848.998ms, respectively. It was able to efficiently deal with wrinkles, lighting effects and lessen the complexity of features and was much faster and more accurate than current methods. Conclusion: It is proven in the current study that wrinkles in the FER frameworks enhance the classification accuracy and emotion strength prediction greatly, and thus the offered approach is appropriate to be applied to real time applications, such as mental health monitoring.