06096883

j

06096883 Zhao, Ya-Ying Tan, Yan-Qi Ma, Xiao-Hu Single sample face recognition based on sample augment and improved 2DPCA. J. Comput. Appl. 31, No. 10, 2728-2730 (2011). 2011 Science Press, Beijing ZH single sample face recognition sample augment within-class average value Two Dimensional Principal Component Analysis (2DPCA)

doi:10.3724/SP.J.1087.2011.02728

Summary: As most of the face recognition techniques will suffer serious performance drop when there is only one training sample per person, a face recognition algorithm based on sample augment methods and improved Two Dimensional Principal Component Analysis (2DPCA) was proposed. By analyzing the advantage and disadvantage of various sample augment methods, some of them were combined to synthesize virtual samples in order to make full use of the single training image. Improved 2DPCA was chosen to extract the feature of the synthetic virtual samples. The training samples were divided into sub-blocks and then the covariance matrix was constructed by these sub-blocks which were normalized by the within-class average value in each sub-block. The experimental results on ORL and Yale face database indicate that the performance of the proposed algorithm is better than those of general 2DPCA and the method only using sample augment.