Abstract
In this paper we compare the performance of Legendre moments, Zernike moments and Pseudo-Zernike moments in feature extraction for iris recognition. We have increased the moment orders until the best recognition rate is achieved. Robustness of these moments in various orders has been evaluated in presence of White Gaussian Noise. Numerical results indicate that recognition rate by the Legendre, Zernike and Pseudo-Zernike moments in higher orders are approximately identical. However, average computation time for feature extraction is 4.5, 18 and. 75 seconds respectively for the Legendre, Zernike and Pseudo-Zernike moments of order 14. On the other hand, the result indicates the Legendre moment is more robust than the others against the white Gaussian noise
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