Eigenvectors of Covariance Matrix Using Row Mean and Column Mean Sequences for Face Recognition
Face recognition has been a fast growing, challenging and interesting area in real-time applications. A large number of face recognition algorithms have been developed from decades. Principal Component Analysis (PCA) is one of the most successful techniques that has been used in face recognition. Four criteria for image pixel selection to create feature vector were analyzed: the first one has all the pixels considered by converting the image into gray plane, the second one is based on taking row mean in RGB plane of face image, the third one is based on taking column mean in RGB plane finally, the fourth criterion is based on taking row and column mean of face image in RGB plane and feature vector were generated to apply PCA technique.