An accurate real-time method to detect the smile facial expression

The rapid advancement of technology has significantly improved our ability to interact with electronic devices and derive meaning from this interaction. One crucial aspect of human-environment interaction is emotion recognition, which allows us to understand and appropriately respond to emotional cues. Among these cues, a smiling facial expression represents contentment and a positive response to situations, usually indicating happiness. Facial expression recognition techniques have been extensively utilized by researchers and scientists in both academic and commercial settings because the analysis of users’ facial expressions can provide valuable insights into computer system behavior and usability, making it an important area of research. In this paper, we propose a method for detecting smile facial expressions by focusing on the mouth region, which plays a vital role in smile characterization and identification. Our approach innovates utilizing normalized facial reference points, commonly known as landmarks, as input for machine learning classifiers. These normalized landmarks are two-dimensional coordinates, enabling low-cost and real-time processing, particularly suitable for devices with limited processing capabilities, such as cellphones. Remarkably, our method exhibits a real-time performance accuracy exceeding 95%. Our research contributes to producing more consistent and accurate outcomes in smile detection, surpassing existing results from the literature.