Face recognition using principal component analysis and wavelet packet decomposition

In this article we propose a novel Wavelet Packet Decomposition (WPD)-based modification of the classical Principal Component Analysis (PCA)-based face recognition method. The proposed modification allows to use PCA-based face recognition with a large number of training images and perform training much faster than using the traditional PCA-based method. The proposed method was tested with a database containing photographies of 423 persons and achieved 82-89% first one recognition rate. These results are close to that achieved by the classical PCA-based method (83-90%).