Real Time Facial Expression Recognition for Nonverbal Communication

This paper represents a system which can understand and react appropriately to human facial expression for nonverbal communications. The considerable events of this system are detection of human emotions, eye blinking, head nodding and shaking. The key step in the system is to appropriately recognize a human face with acceptable labels. This system uses currently developed OpenCV Haar Feature-based Cascade Classifier for face detection because it can detect faces to any angle. Our system can recognize emotion which is divided into several phases: segmentation of facial regions, extraction of facial features and classification of features into emotions. The first phase of processing is to identify facial regions from real time video. The second phase of processing identifies features which can be used as classifiers to recognize facial expressions. Finally, an artificial neural network is used in order to classify the identified features into five basic emotions. It can also detect eye blinking accurately. It works for the active scene where the eye moves freely and the head and the camera moves independently in all directions of the face. Finally, this system can identify the natural head nodding and shaking that can be recognized in real-time using optical flow motion tracking and find the direction of head during the head movement for nonverbal communication