Facial expression recognition using Eigen face approach

Emotion recognition via identification of face emotion is the primary areas of research between human and the machine communication. In order to identify facial emotion, one device must locate different variabilities of human face including color, stance, emotions, direction, and so on. Initially, the human facial expressions are recognized by the various features such as eye, nose, mouth, etc. must be detected and then classified using an appropriate expression recognition classifier compared to the qualified results. In this analysis a method of identification of human facial expression is modeled on its own hands. The approach proposed uses the color model HSV (Hue-Saturation-Value) to identify the facial image. Principal Component Analysis reduce the size of the space, later the test image is projected on the space. The Euclidean distance between the test image and the mean individual of the training data and categorized the phrases. For testing purposes, a standardized dataset is used. The device uses the gray representations of the face to categorize five simple emotions including surprise, sadness, anxiety, indignation and satisfaction.