Palmprint authentication using modified legendre moments

The resolution of the acquired biometric image is considered as one of the primary factors affecting the performance of a biometric authentication system. For low quality images, a powerful feature extraction method is very essential. Orthogonal Legendre moments are used in several pattern recognition and image processing applications for feature extraction. Translation and scale invariant Legendre moments are achieved directly by using the Legendre polynomial. However, this method does not yield a rotational invariant form. In this paper we propose a palmprint verification system in which the 2D Legendre moments are represented as a linear combination of geometric moment invariants. Geometric moments are invariant to translation, non-uniform scaling and rotation. The modified Legendre moments are used for feature extraction and a weighted fusion technique is used to fuse the matching scores of the sub-images. The results obtained using a Baye’s classifier indicate an impressive prediction accuracy of 98%, validating the choice of low order Legendre moment for effective palmprint verification.