Affine moment invariants: a new tool for character recognition
Pattern Recognition Letters
Generation of moment invariants and their uses for character recognition
Pattern Recognition Letters
Complete Sets of Complex Zernike Moment Invariants and the Role of the Pseudoinvariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric Based Cryptographic Key Generation from Faces
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
A Novel Cryptosystem Based on Iris Key Generation
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 04
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Numerical accuracy of moment invariants is very important for reliable feature extraction in biometric recognition and cryptosystems. This paper presents a novel approach to derive accuracy enhanced moment invariants that are invariant under translation, rotation, scaling, pixel interpolation and image cropping. The proposed approach defines a cosine based central moment and adopts a windowing mechanism to enhance accuracy of moment invariants under translation, rotation, scaling, pixel interpolation and image cropping. It derives moment invariants by extending the knowledge used in Hu's and Maitra's approaches. Simulation results show that the proposed moment invariants highly accurate than Hu's and Maitra's moment invariants.