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Identification of humans through biometric technologies is becoming common. Different biometric technologies like finger, face, voice, iris recognition, etc. use different behavioral or psychological characteristics of humans for recognition. Early systems used to have password and ID cards for verification but it has two major problems of forgotten passwords and stolen ID cards. Biometrics provided solution to these problems. Among the all biometrics, iris recognition has achieved highest recognition accuracy. An iris is a colored area between dark pupil and bright sclera. Iris has unique characteristics like stability of iris patterns throughout life time, not surgically modifiable. Its probability of uniqueness among all humans has made it a reliable and efficient human recognition technique. It can be used in many applications like controlled access, airports, ATM, etc. Many researchers developed iris recognition systems that are different to each other with respect to feature extraction.
Iris structure has complex and plentiful textures which can be extracted as features for iris coding. We present a new representation of iris coding by using 2D wavelet. The proposed scheme of feature extraction is to use the multi-level coefficients of decomposition parts of image via wavelet. A feature vector consists of 5-level decomposition coefficients along vertical and horizontal direction. From these coefficients a compact binary code is generated.
Index Terms: Matlab, source, code, iris, recognition, identification, wavelet, wavelets, decomposition.
Figure 1. Multilevel wavelet decomposition.
A simple and effective source code for Iris Identification Using 2D Wavelet.
Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox and Matlab Wavelet Toolbox are required.
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The authors have no relationship or partnership
with The Mathworks. All the code provided is written in Matlab
language (M-files and/or M-functions), with no dll or other
protected parts of code (P-files or executables). The code was
developed with Matlab R2006a. Matlab Image Processing Toolbox and Matlab Wavelet Toolbox are required.
The code provided has to be considered "as is" and it is without any kind of warranty. The
authors deny any kind of warranty concerning the code as well
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