Wavelet Based Image De-noising to Enhance the Face Recognition Rate

In this paper a comparison between face recognition rate with noise and face recognition rate without noise is presented. In our work we assume that all the images in the ORL faces database are noisy images. We applied the wavelet based image de-noising methods to this database and created new databases, then the face recognition rate are calculated to them. Three experiments are given in our paper. In the first experiment different wavelet methods with different level of decomposition (up to ten decompositions) are used for de-noising the ORL database and the comparison is done when Principal Components Analysis (PCA) is applied to evaluate the verification rate. In the second experiment de-noising different sets of ORL database with methods that have best performance in levels (1, 2, 3, and 10) is done (as a result from experiment 1). In the third experiment we implement the proposed Haar10 method on PCA, Linear Discriminate Analysis (LDA), Kernel PCA, Fisher Analysis (FA) face recognition methods and the recognition rates are evaluated for both the noisy and de-noisy databases.