Iris Recognition Based on Pulsed Coupled Neural Networks

Hai Ying Deng; Dong Ming Zhou; Ren Can  Nie; Xiang Li; Hong Mei Li

doi:10.4028/www.scientific.net/AMM.380-384.2637

Paper Titles

Software Design of Smart Car Tracking with Camera
p.2619

An Improved Block Cryptosystem Based on Iterating Delayed Chaotic Neural Networks
p.2623

Comparative Study on Currently Popular Network Databases
p.2629

The Searching of Decision Tree Based on Data Mining
p.2633

Iris Recognition Based on Pulsed Coupled Neural Networks
p.2637

Intelligent Substation Construction Program and the Whole Life-Cycle Information Platform Technology Research Based on Internet of Things
p.2641

Research on E-Commerce Software Framework of MVC Mode Based on .NET
p.2645

Research on the Optimal Design of Soccer Robot Based on the Mechanical Analysis
p.2649

Development of Driving Training Simulator of a Caterpillar Vehicle
p.2653

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 380-384Iris Recognition Based on Pulsed Coupled Neural...

Iris Recognition Based on Pulsed Coupled Neural Networks

Abstract:

Based on Pulsed Coupled Neural Network Model, respectively to study the one-dimensional time series, row time series, column time series will keep some invariance by image migration. To apply this nature in iris recognition field, found that to extraction features using row time series method, which can resist rotation changes between the same samples, and keep enough details to distinguish between different samples. Compared with the traditional iris recognition method which using iris phase structure encoded, the results show that row time series method, is less sensitive for phase, and can resist rotation changes better, is effective.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 380-384)

Pages:

2637-2640

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.380-384.2637

Citation:

Cite this paper

Online since:

August 2013

Authors:

Hai Ying Deng, Dong Ming Zhou, Ren Can Nie, Xiang Li, Hong Mei Li

Keywords:

Invariance, Iris Phase Structure Encoded, Iris Recognition, Pulsed Coupled Neural Network, Time Series

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Daugman J G. High confidence of visual recognition of persons by a test of statistical independence[J]. IEEE Trans. On Pattern Analysis and Machine Intelligence, 1993, 15(11): 1148~1161.

DOI: 10.1109/34.244676

Google Scholar

[2] Daugman J G. The Importance of being random: Statistical principles of iris recognition [J]. Pattern Recognition, 2003, 36(2): 279~291.

DOI: 10.1016/s0031-3203(02)00030-4

Google Scholar

[3] Daugman J G. High confidence Recognition of Persons by Iris Patterns [J]. IEEE Trans. On Pattern Analysis and Machine Intelligence, 2001, 15(11): 1148~1161.

DOI: 10.1109/34.244676

Google Scholar

[4] Daugman J G. C. Downing, Demodulation, predictive coding, and spatial vision [J]. J. Opt. Soc. Amer. A 12 (4) 1995, 641-660.

DOI: 10.1364/josaa.12.000641

Google Scholar

[5] Daugman J G. Two-dimensional spectral analysis of cortical receptive field profiles [J]. Vision Res. 20(10), 1980, 847-856.

DOI: 10.1016/0042-6989(80)90065-6

Google Scholar

[6] Daugman J G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters [J]. J. Opt. Soc. Amer. A 2(7), 1985, 1160-1169.

DOI: 10.1364/josaa.2.001160

Google Scholar

[7] R. Eckhorn, H. J. Reitboeck, M. Arndt, P.W. Dicke, Feature linking via synchronization among distributed assemblies: simulation of result from cat visual cortex [J]. Neutral computation 2(3), 1990, 293-307.

DOI: 10.1162/neco.1990.2.3.293

Google Scholar