Zernike moments-based fingerprint recognition using weighted-support vector machine

Abstract

Moments play an important role in image analysis and invariant pattern recognition. There are two types: orthogonal moments and non-orthogonal moments. Orthogonal moments perform better than non-orthogonal moments, they have properties such as robustness to image noise and geometrical invariant properties such as scale, rotation and translation. In this paper, an improvement in fingerprint recognition is done by using the Non-subsampled contourlet transform (NSCT) and the Zernike moments (ZMs). NSCT decomposes the fingerprint images into NSCT subbands. Thereafter, ZMs are used to evaluate the features of fingerprint images. Thereafter, feature selection technique is applied to select potential features from the obtained features using coefficient of determination. Thereafter, a well-known weighted-support vector machine is also used to train and test the evaluated features. Extensive experiments reveal that the proposed technique achieves significant improvement over the existing techniques in terms of accuracy, sensitivity, specificity, [Formula: see text]-measure, kappa statistics and execution time.