Single Training Sample Face Recognition Based on Block 2DPCA and 2DLDA

Two-dimensional linear discriminant analysis (2DLDA) has achieved great success in face recognition, however, it fails to work for single training sample face recognition, since it need more than one sample per person to estimate the within-class scatter. This paper proposes a novel single training sample face recognition algorithm based on Block 2DPCA+2DLDA. At first, the original images are divided into some sub-images, according to the locations of the sub-images they are divided into multiple sample sets, 2DPCA is applied on each sample set for the first recognition. The test samples which have been identified after the first recognition are added to the original training sample set as the samples of known class labels, and the original single training sample set will become a multiple training samples set. Finally 2DLDA is used in the new training sample set and test set for the second recognition, which is to identify the images that are not recognized on the first time. The Block 2DPCA+2DLDA algorithm is tested on the ORL face database, the experiment results show that the algorithm is better than PCA and 2DPCA algorithms etc.