This paper covers the theory, design, and simulation of facial recognition with fuzzy inference system. The simulation is designed to recognize an individual from a diverse group of people and ultimately find the best matching picture from a dataset. A large group of students with varying facial expressions is collected in order to create a dataset that is split into training and testing pictures so that the simulation can be properly tested throughout the experiment. Facial landmarks are applied in order to retrieve different coordinate points throughout the face so that the facial feature’s relative distances and areas could be calculated for each individual and be fed into the fuzzy system. The fuzzy inference system outputs a value that is used to determine which test picture matches best with the training picture that is initially inputted. The dimensions and the brightness of each picture in the dataset is optimized in order to improve the results. After proper modifications, the facial landmarks are working as expected and the newly optimized results improved with an 80% accuracy rate.