A new algorithm of improved two-dimensional principal component analysis face recognition

Abstract

The traditional two-Dimensional Principal Component Analysis(2DPCA) only extracts the in-line features of data of face image, the direction of feature extraction is relatively simple, and the feature extraction in other directions is not considered. In order to extract the features of the image from multiple angles and provide more abundant information for recognition, a new method of 2DPCA face recognition is proposed. The new algorithm first self-corrects the face image, at the same time, it extracts the low frequency information of the image, and then it uses the Perceptual hash technique to obtain the ‘fingerprint’ of the image. Then, the new algorithm will rotate multi-angle images from the self-corrected face images and extract the features separately to get multi-angle feature information. Finally, the training sample pictures are classified again for each category, and the images of similar expressions or features are classified to retain the special expressions or features. The numerical experiments in the ORL human face databases show that the improved algorithm is superior to the traditional 2DPCA algorithm.