Iris Recognition: An Emerging Biometric Technology


Iris recognition

Iris Recognition System

Iris Recognition Based on Neural Networks

Phase-Based Iris Identification

Moving Average Filter Iris Recognition

Iris Identification Based on 2D Wavelet

DCT-Based Iris Recognition

Hybrid Iris Recognition

LBP Iris Recognition

Iris Recognition Based on Genetic Algorithms

One-to-One Iris Recognition System

External resources

Advanced Source Code .Com

Neural Networks .It

Genetic Algorithms .It

Face Recognition .It

Iris Recognition Based on Genetic Algorithms

Download now Matlab source code
Requirements: Matlab, Matlab Image Processing Toolbox.

There has been a rapid increase in the need of accurate and reliable personal identification infrastructure in recent years, and biometrics has become an important technology for the security. Iris recognition has been considered as one of the most reliable biometrics technologies in recent years. The human iris is the most important biometric feature candidate, which can be used for differentiating the individuals. For systems based on high quality imaging, a human iris has an extraordinary amount of unique details. Features extracted from the human iris can be used to identify individuals, even among genetically identical twins. Iris-based recognition system can be noninvasive to the users since the iris is an internal organ as well as externally visible, which is of great importance for the real-time applications.

We have developed an iris recognition method based on genetic algorithms (GA) for the optimal features extraction. The accurate iris patterns classification has become a challenging issue due to the huge number of textural features extracted from an iris image with comparatively a small number of samples per subject. The traditional feature selection schemes like principal component analysis, independent component analysis, singular valued decomposition etc. require sufficient number of samples per subject to select the most representative features sequence; however, it is not always realistic to accumulate a large number of samples due to some security issues. We propose GA to improve the feature selection by optimal filtering.

This code is based on Libor Masek's excellent implementation available here.

Libor Masek, Peter Kovesi. MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering, The University of Western Australia, 2003.

All tests were performed with CASIA Iris Image Database available at

Index Terms: Matlab, source, code, iris, recognition, matching, GA, genetic, algorithms, algorithm.

Release 1.0 Date 2010.05.05
Major features:

Iris Recognition . It Luigi Rosa mobile +39 3207214179