Nonlinear Feature Extraction Approaches with Application to Face Recognition over Large Databases

The extraction of required features from the facial image is an important primitive task for face recognition. This paper evaluates different nonlinear feature extraction approaches, namely wavelet transform, radon transform and cellular neural network, (CNN). The scalability or the linear subspace techniques is limited as The computational load and memory requirements increase dramatically with the large database. In this work, the combination of radon and wavelet transform based approach is used to extract the multi-resolution features. which are invariant 10 facial expression and illumination conditions. The efficiency of,be stated wavelet and radon based nonlinear approaches over the it databases is demonstrated with the simulation results performed over the FERET database. This paper also presents the use of CNN in extracting the nonlinear facial features in improving the recognition rate as well as computational speed compared to other stated nonlinear approaches over the ORL database.