Facial Expression Recognition using SVC Classification&INGI Method

Facial expression analysis is the important area of Human Robot Interaction (HRI) because facial expressions represent human emotions. Most existing researches in facial expression analysis mainly focused on recognizing extreme facial expressions. In existing work they introduced a tensor perceptual color framework (TPCF) for facial expression recognition (FER), which is based on information contained in color facial images. Humans can perform expression recognition with a remarkable robustness without conscious effort even under a variety of adverse conditions such as partially unmarked faces, different appearances and poor illumination. To avoid this proposed face recognition system consists of a novel illumination-insensitive pre-processing method for eliminating the illumination. First, in the pre-processing stage, a face image is transformed into an illumination-insensitive image, called an �integral normalized gradient image, � by normalizing and integrating the smoothed gradients of a facial image. The features are classified by using Support Vector clustering (SVC) classifier to avoid the complexity. The objective of this paper is to apply Support Vector Machines to the problem of classifying emotion on images of human faces.