Ten lectures on wavelets
IEEE Spectrum
Time-frequency analysis: theory and applications
Time-frequency analysis: theory and applications
Neural networks for pattern recognition
Neural networks for pattern recognition
Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns
International Journal of Computer Vision
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
Iris Recognition Using Collarette Boundary Localization
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Biometrics: Personal Identification in Networked Society
Biometrics: Personal Identification in Networked Society
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iris recognition using fourier-wavelet features
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
UBIRIS: a noisy iris image database
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Iris recognition based on bidimensional empirical mode decomposition and fractal dimension
Information Sciences: an International Journal
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Iris recognition is known as an inherently reliable technique for human identification. Empirical Mode Decomposition (EMD), an adaptive multi-resolution decomposition technique, appears to be suitable for non-linear, non-stationary data analysis. Based on EMD, a fully data-driven method without using any pre-determined filter or wavelet function, an iris recognition scheme is presented by modifying EMD as a low-pass filter to analyze the iris images. To evaluate the efficacy of the proposed approach, three different similarity measures are used. Experimental results show that three metrics have all achieved promising and similar performance. Therefore, the proposed method demonstrates to be feasible for iris recognition and EMD is suitable for feature extraction.