Wavelet Based Sub-space Features for Face Recognition

In this paper we propose features based on sub-space projection methods using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) on wavelet sub-band for face recognition. Wavelet based sub-band decomposition helps to reduce the size of image, and the approximate image obtained in the low-low (approximate) band is used here to apply sub-space projection methods. This improves the speed of feature extraction process without compromising the recognition performance. Classification of the faces based on the extracted features was carried out by using a Linear Discriminant function based classifier on Olivetti Research Laboratory (ORL) image database. Different level of wavelet decomposition is carried out and recognition performance evaluated. Highest recognition was achieved at 3 level wavelet decomposition using ICA. The proposed scheme uses minimum number of features and the recognition results obtained show an improvement of about 0.5% over some of the existing schemes with lower computation cost.

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