A Hybrid DWT, PCA and ICA Features for Face Recognition using ANN

Face recognition plays an important role in biometrics base personal identification. The biometrics recognition technique acts as an efficient method and wide applications in the area of information retrieval, automatic banking, and control of access to security areas and so on. The proposed method is based on Principal Component Analysis (PCA) of image with a combination of details of DWT. This approach reduces the storage requirement and computation time while preserving the data. The proposed scheme exploits feature extraction capabilities of the Discrete Wavelet Transform Decomposition and invokes certain normalization techniques that increase its robustness to variations in facial geometry and illumination. Traditionally, to represent the human face, PCA is performed on the whole facial image. Neural Network and K-NN Classifier are used to classify the features and the similarity measure is done by Euclidian Distance. Experimental results show that the proposed method is effective and possesses several desirable properties when it compared with many existing algorithm. The approach PCA-DWT-ICA-hybrid is evaluated on MATLAB using Yale face database.