  Emotions are best way of communicating information; and sometimes it carry
more information than words. Recently, there has been a huge interest in
automatic recognition of human emotion because of its wide spread application
in security, surveillance, marketing, advertisement, and human-computer
interaction. To communicate with a computer in a natural way, it will be
desirable to use more natural modes of human communication based on voice,
gestures and facial expressions. In this paper, a holistic approach for facial
expression recognition is proposed which captures the variation in facial
features in temporal domain and classifies the sequence of images in different
emotions. The proposed method uses Haar-like features to detect face in an
image. The dimensionality of the eigenspace is reduced using Principal
Component Analysis (PCA). By projecting the subsequent face images into
principal eigen directions, the variation pattern of the obtained weight vector
is modeled to classify it into different emotions. Owing to the variations of
expressions for different people and its intensity, a person specific method
for emotion recognition is followed. Using the gray scale images of the frontal
face, the system is able to classify four basic emotions such as happiness,
sadness, surprise, and anger.
