Face recognition using DWT and eigenvectors

A Face recognition system using Discrete Wavelet Transform (DWT) & eigenvectors is proposed in this paper. Each face image is decomposed as four sub bands using DWT. These four sub bands are approximation sub band (LL), horizontal detail sub band (LH), vertical detail sub band (HL), and diagonal detail sub band (HH). HH sub band is very fragile. HH sub band is useful to distinguish the images in the database. Hence, HH band is exploited for face recognition. HH sub band is further processed using Principal Component Analysis (PCA). PCA extracts the relevant information from confusing data sets. Further, PCA provides a solution to reduce the higher dimensionality to lower dimensionality. Feature vector is generated using DWT and PCA. In PCA technique, sub images are rearranged into vertically and horizontally matrices. Experiments are performed on YALE database. Results indicate that the proposed method gives better average recognized rate and less computation time compared to the existing methods available in the literature.