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Face Recognition Using Moments and Wavelets

The field of face recognition has been explored a lot and the work is still going on. In this paper we have proposed a novel approach for face recognition using moments. Four feature extraction methods have been used: Hu moments, Zernike moments, Legendre moments and Cumulants. Hu moments include a set of seven moments which are derived from the conventional geometric moments. These moments are invariant against rotation, scaling and translation. Legendre and Zernike moments have an orthogonal basis set and can be used to represent an image with minimum amount of information redundancy. These are based on the theory of orthogonal polynomials and can be used to recover an image from moment invariants. Cumulants are sensitive to image details and therefore are suitable for representing the image features. For feature extraction, moments of different orders are calculated which form the feature vectors. The feature vectors obtained are stored in the database and are compared using three different classifiers. In case of cumulants, we have calculated the bispectrum of images and compressed it using wavelets.