Face Recognition via Multi linear Principal Component Analysis and Two-Dimensional Extreme Learning Machine

摘要

Because of face images are naturally two-dimensional data, there have been several 2D feature extraction methods to deal with facial images while there are few 2D effective classifiers. In this work, by using a linear tensor projection, a new two-dimensional classifier based on extreme learning machine is introduced. Due to the proposed algorithm can classify matrix data directly without vectorizing them, the intrinsic structure information of the input data can be reserved. In addition, discriminative features sets are generated using MPCA to ascertain classification accuracy. Finally, Extensive experiments are carried out on two face databases and the results are compared against state-of-the-art techniques. It is demonstrated that the proposed algorithm MPCA plus 2D-ELM achieves better recognition performance.