Face Recognition using Wavelet , PCA , and Neural Networks

This work presents a method to increased the face recognition accuracy using a combination of Wavelet, PCA, and Neural Networks. Preprocessing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA. During the classification stage, the Neural Network (MLP) is explored to achieve a robust decision in presence of wide facial variations, also we have used RBF Neural Network but results show that MLP Neural Network outperforms RBF. The computational load of the proposed method is greatly reduced as comparing with the original PCA based method. Moreover, the accuracy of the proposed method is improved.