Face Recognition Based on Data Field

Principal component analysis(PCA) is one ofthe important methods for extracting the main features offace images. However, it mainly considers the values of the pixels in the images while ignoring the relations between one pixel and other pixels around it. In fact, the pixels of the face images are not independent of each other. Based on this, we propose a new method for extracting the features of face images based on the datafield method whichconsiders the relations between pixels. Furthermore, we combine the datafield with PCA(dfPCA) to attract more accuratefeaturesfromface images. In the experiments, we first analyze the contours of face images with datafiled method. Then, we applythe dfPCA methods on two real world datasets, ORL and yale, for feature extraction respectively. The results show that the recognition rate based on the dfPCA method is better than the PCA method, which demonstrates the feasibility of datafield method for extracting facial features.