New Approach of Estimating Sarcasm Based on the Percentage of Happiness of Facial Expression Using Fuzzy Inference System

The procedure of determining whether micro expressions are present is accorded a high priority in the majority of settings. This is due to the fact that despite the best attempts of the person, these expressions will always expose the genuine sentiments that are buried under the surface. The purpose of this study is to provide a novel approach to the problem of measuring sarcasm by using a fuzzy inference system. The method involves analysing a person's facial expressions to evaluate the degree to which they are taking pleasure in something. It is feasible to distinguish five separate areas of a person's face, and precise active distances may be determined from the outline points of each of these regions. This category includes the brows on both sides of the face, as well as the eyes and lips. In order to arrive at a representation of an individual's degree of happiness while working within the parameters of the fuzzy inference system that has been provided, membership functions are first applied to computed distances. After that, the findings from the membership functions are put to use in yet another membership function so that an estimate of the sarcasm percentage may be derived from them. The suggested method is validated by using photos of human faces taken from the SMIC, SAMM, and CAS(ME) 2 datasets, which are the industry standards. This helps to guarantee that the method is effective