Principal Component Analysis (PCA) is one of the key methods for solving data analysis problems with a large number of dimensions like face recognition. Nevertheless, classical PCA is based on L2-Norm which is very sensitive to noises. Recently, a new robust PCA approach has been proposed by replacing L2-Norm with L1-Norm (PCA-L1). However, PCA-L1 requires a lot of time to calculate the projection bases. To solve this problem, we propose to use a wavelets feature extraction method as pre-processing step to face recognition. Extensive experiments on two well-known face image datasets namely ORL and Georgia Tech Face-Database(GTFD), show that the proposed approach minimizes execution time and has a recognition rate up to 96.7% for ORL and 85% for GTFD.