High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
A systematic method for efficient computation of full and subsets Zernike moments
Information Sciences: an International Journal
| Hi-index | 0.00 |
Iris recognition, a relatively new biometric technology, has great advantages, such as variability, stability and security, thus it is the most promising for high security environments For iris recognition it is desirable to obtain an iris representation invariant to translation, scale and rotation Translation invariance and approximate scale invariance usually can be easy achieved by pre-processing, but rotation invariance is still a problem In this paper, a new iris recognition algorithm is proposed, which adopts Zernike's moment invariants to extract iris moment-based rotation invariant features without any iris rotation adjustment These invariant features are selected automatically based on the discrimination measure defined for the invariant features Experimental results show that the proposed method has an encouraging performance In particular, it achieves a lower Equal Error Rate than which in [2] proposed by Daugman without rotation adjustment.