An Efficient way of Emotion and Gesture Recognition using Deep Learning Algorithms

Researchers in psychology, computer science, linguistics, neurology, and allied fields have become more interested in a human-computer interface system for autonomous face recognition or facial expression recognition. An Automatic Facial Expression Recognition System (AFERS) has been suggested in this study. Three steps comprise the proposed methodology: face detection, feature extraction, facial expression recognition, and gesture recognition. Face detection's initial steps include YCbCr-color skin colour detection, illumination correction for uniformity on the face, and morphological procedures for maintaining the necessary face region. Using the AAM (Active Appearance Model) approach, the first phase's output is utilized to extract facial features such as the mouth, nose, and eyes. Automatic facial expression recognition, the third stage, entails straightforward. Using the AAM (Active Appearance Model) approach, the first phase's output is utilized to extract facial features such as the mouth, nose, and eyes.