Invariant Feature Extraction From Fingerprint Biometric Using Pseudo Zernike Moments
To represent the large amount of data in the biometric images an efficient feature extraction method is needed. Further biometric image acquisition is subject to deforming processes such as rotation, translation and scaling. Hence, it is also required that the image representation be invariant to the deformations and sustain the discriminating features. Considering the trade off between the discriminating power and the invariants, moments are a very qualifying object descriptor. In this paper, the authors have used Pseudo Zernike moments to create invariant feature vectors for the Finger print biometric.