Development a New Algorithm for Iris Biometric Recognition
@article{Hentati2012DevelopmentAN, title={Development a New Algorithm for Iris Biometric Recognition}, author={Raida Hentati and Manel Hentati and Mohamed Abid}, journal={International Journal of Computer and Communication Engineering}, year={2012}, pages={283-286} }
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References
SHOWING 1-6 OF 6 REFERENCES
A machine-vision system for iris recognition
- Computer ScienceMachine Vision and Applications
- 2005
A prototype system for personnel verification based on automated iris recognition for noninvasive biometric measurement is described, in which the system exhibits flawless performance in the evaluation of 520 iris images.
Analysis of textual images using the Hough transform
- Computer ScienceMachine Vision and Applications
- 2005
Methods for handling several discretization problems that arise in mapping the rectangular image space to the (ρ, Θ) accumulator array are described.
Iris Feature Extraction Using 2D Phase Congruency
- Computer ScienceThird International Conference on Information Technology and Applications (ICITA'05)
- 2005
This paper made some experimental try to extract the iris feature using the 2D phase congruency, which invariant to changes in intensity or contrast, to try to avoid those problems of natural illumination or other variant conditions.
The Human Iris Structure and Its Usages
- Philosophy
- 2000
This paper is devoted to the human iris structure and to the practical usages of the knowledge of its structure, which are identiication of a person and iridology.
A human identification technique using images of the iris and wavelet transform
- PhysicsIEEE Trans. Signal Process.
- 1998
A new approach for recognizing the iris of the human eye is presented, and the resulting one-dimensional signals are compared with model features using different dissimilarity functions.
Person Identification Technique Using Human Iris Recognition
- Computer Science
- 2002
A new iris recognition system that implements (i) gradient decomposed Hough transform / integro-differential operators combination for iris localization and (ii) the "analytic image" concept (2D Hilbert transform) to extract pertinent information from iris texture is examined.