Supervised facial recognition based on multiresolution analysis with radon transform

A new supervised facial recognition approach based on the integration of Two Dimensional Discrete Multiwavelet Transform (2D DMWT), 2D Radon Transform (2D RT), and 3D DWT is proposed. In the feature extraction step, 2D DMWT is used to extract the useful information from the image. The extracted features are then aligned using 2D RT and localized in one single band using 3D DWT. The resulting features are fed into a Neural Network for both training and testing. The proposed algorithm is tested on different databases, namely, ORL, YALE, and subset fc of FERET. It is shown that the proposed approach can significantly improve the recognition rate and the storage requirements of the overall recognition system.