Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Half-Eye Wavelet Based Method for Iris Recognition
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Iris recognition for partially occluded images: methodology and sensitivity analysis
EURASIP Journal on Applied Signal Processing
Multiresolution circular harmonic decomposition
IEEE Transactions on Signal Processing
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
Noisy Iris Verification: A Modified Version of Local Intensity Variation Method
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
A region-based Iris feature extraction method based on 2D-wavelet transform
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Robust iris verification based on local and global variations
EURASIP Journal on Advances in Signal Processing
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In this paper preliminary results of a new iris recognition algorithm using Gauss-Laguerre filter of circular harmonic wavelets are presented. Circular harmonic wavelets (CHWs) applied in this paper for iris pattern extraction, are polar-separable wavelets with harmonic angular shape. The main focus of this paper is on iris coding using Gauss-Laguerre CHWs which constitute a family of orthogonal functions satisfying wavelet admissibility condition required for multiresolution pyramid structure. It is shown that Gauss-Laguerre wavelets having rich frequency extraction capabilities are powerful tools for coding of iris patterns. By judicious tuning of Laguerre parameters, a 256-byte binary code is generated for each iris. A fast matching scheme based on Hamming distance is used to compute the similarity between pairs of iris codes. Preliminary experimental results on CASIA and our database indicate that the performance of the proposed method is highly accurate with zero false rate and is comparable with Daugman iris recognition algorithm well publisized in literature.