A biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Among the features measured are; face fingerprints, hand geometry, handwriting, iris, retinal, vein, and voice. The need for biometrics can be found in federal, state and local governments, in the military, in commercial applications, network, passport control, and automatic teller machine.
Pin numbers, email passwords, credit card numbers, and protected premises access numbers all have something in common. All of them are a key to your identity, and all of them can easily be stolen or guessed so identity management accomplished by individual recognition. Iris recognition is regarded as the most reliable and accurate biometric identification system available. The iris recognition system consists of an automatic segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantized to encode the unique pattern of the iris into a bit-wise biometric template.
For determining the recognition performance of the system CASIA Iris Interval (left and right images) database of digitized grey scale eye images was used. The database is divided into training and testing database. In training database, I use first image from each folder for left and right eye where each folder contains more than or equal two
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images and I removed bad segmented images where database contains correct segmentation images only. In testing database, I use the remaining images of database without bad segmented images to improve recognition rate. The Hamming distance was employed for classification of iris templates, and two templates were found to match if a test of statistical independence was failed. The system performed with perfect recognition on a set of 1332 left eye images resulted in false accept and false reject rates of 1.218% and 2.062% respectively and 1305 right images resulted in false accept and false reject rates of 1.357% and 2.810% respectively . Therefore, iris recognition is shown to be a reliable and accurate biometric technology. |
