Facial expression detection using principal component analysis

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 Principal Component Analysis (PCA) is proposed for facial expression detection. Initially the eigenspace was created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaces have been selected using Principal Component Analysis (PCA). With these eigenfaces the input test images are classified based on Euclidian distance. 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. There are 30 persons in database. The database is kept in the train folder which contains each person having all his/her photographs. Another database was also prepared for testing phase by taking 5 photographs of 30 persons in different expressions and viewing angles but in similar conditions ( such as lighting, background, distance from camera etc.). And these database images were stored in test folder