Real Time Facial Expression Analysis Using PCA

Face recognition has been very important issue in computer vision and pattern recognition over the last several decades. One difficulty in face recognition is how to handle the variations in the expression, pose and illumination when only a limited number of training samples are available. In this project, we performed Real Time Facial Expression Analysis Using PCA. Initially the Eigen space was created with Eigen values and eigenvectors. From this space, the Eigen faces are constructed, and the most relevant Eigen faces have been selected using Principal Component Analysis (PCA). With these Eigen faces the input test images are classified based on Facial Expression Classifier. The proposed method was carried out by taking the picture database. The database was obtained with 10 photographs of each person at different expressions. These expressions can be classified into some discrete classes like happy, anger, disgust, sad and neutral. Absence of any expression is the neutral expression. The database is kept in the train folder which contains each person having all his/her photographs.