An Efficient Face Recognition System Using DWT-ICA Features

Multiresolution representations and Subspace analysis have been widely accepted in the face recognition systems. This research paper combines the benefits and presents the feature extraction method using Discrete Wavelet Transform (DWT) and Independent Component Analysis (ICA). The DWT provides multiresolution representations and are effective in analyzing the information content of the image and generates the feature sets for images from individual wavelet sub bands. The feature images constructed from Wavelet Coefficients (Cohen Daubechies Feauveau (CDF-9/7)) are used as a feature vector for ICA based subspace analysis. ICA is an unsupervised statistical method reduces the dimensionality of the feature vector and extracts the information in the higher-order relationship of pixels. ICA method has been used to find statistically independent basis images or coefficients for the face images to deal with the sensitivity to higher order image statistics. Reduced feature vector are used for further classification using Euclidean Distance (ED) classifier. The proposed scheme has been tested on the standard and real-time Database and the results have been reported. It was observed that the proposed method classifies the images with better accuracy and outperforms the existing methods.