Adaptive Principal Component Analysis Based Wavelet Transform and Image De-noising for Face Recognition Applications

In this paper a novel face recognition approach based on Adaptive Principal Component Analysis (APCA) and de-noised database is produced. The aim of our approach is to overcome PCA disadvantages especially the two limitations of discriminatory power poverty and the computational load complexity, by producing a new adaptive PCA based on single level 2-D discrete wavelet transform using Daubachies filter mode. All face images in ORL database are transformed to JPG file format and are de-noised by Haar wavelet at level 10 of decomposition; the goal is to exhibit the advantage of wavelet over compressed JPG files instead of using origin PGM file format. As a result , our adaptive approach produced good performance in raising the accuracy ratio and reducing both the time and the computation complexities when compared with four other methods represented by standard statistical PCA, Kernel PCA, Gabor PCA and PCA with Back propagation Neural Network (BPNN).