Iris Recognition: An Emerging Biometric Technology

Hybrid Iris Recognition

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

The use of biometric signatures, instead of tokens such as identification cards or computer passwords, continues to gain increasing attention as an efficient means of identification and verification of individuals for controlling access to secured areas, materials, or systems and a wide variety of biometrics has been considered over the years in support of these challenges. Iris recognition is especially attractive due to the stability of the iris texture patterns with age and health conditions. Iris image segmentation and localisation is a key step in iris recognition and plays an essential role the accuracy of matching. We have developed a fast and accurate scheme for iris segmentation. On CASIA Iris Database the average time required for iris detection is 0.1901 seconds.

By adopting an hybrid scheme for features extraction we have achieved an excellent recognition rate of 97.6852% (108 classes, 3 training images and 4 test images for each class, hence there are 324 training images and 432 test images with no overlap between the training and test images).

Index Terms: Matlab, source, code, iris, recognition, segmentation, detection, verification, matching.

Release 1.0 Date 2008.11.24
Major features:

• Hybrid iris recognition
• Fast and accurate iris segmentation
• High recognition rate
• Matching module
• Easy and intuitive GUI
• C code included
• Demo code (protected P-files) available for performance evaluation