  Facial expression analysis is one of the popular fields of research in human
computer interaction (HCI). It has several applications in next generation user
interfaces, human emotion analysis, behavior and cognitive modeling. In this
paper, a facial expression classification algorithm is proposed which uses Haar
classifier for face detection purpose, Local Binary Patterns (LBP) histogram of
different block sizes of a face image as feature vectors and classifies various
facial expressions using Principal Component Analysis (PCA). The algorithm is
implemented in real time for expression classification since the computational
complexity of the algorithm is small. A customizable approach is proposed for
facial expression analysis, since the various expressions and intensity of
expressions vary from person to person. The system uses grayscale frontal face
images of a person to classify six basic emotions namely happiness, sadness,
disgust, fear, surprise and anger.
