Iris Biometric for Person Identification Using Dual- Tree Complex Wavelet Transform
Neelam T. Rakate1, U. A. Patil2

1Miss. Neelam T. Rakate, Department of Electronics Engineering, D.K.T.E, and society’s Textile & Engineering Institute, Ichalkaranji, India.
2Prof. U. A. Patil, Department of Electronics Engineering, D.K.T.E, and society’s Textile & Engineering Institute, Ichalkaranji, India.
Manuscript received on February 05, 2014. | Revised Manuscript Received on February 09, 2014. | Manuscript published on February 18, 2014. | PP: 09-18 | Volume-1, Issue-3, February 2014. | Retrieval Number: C0136021314
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Abstract: Technologies that exploit biometrics have the potential for application to the identification and verification of individuals for controlling access to secured areas or materials. A wide variety of biometrics has been marshaled in support of this challenge. Resulting systems include those based on automated recognition of retinal vasculature, fingerprints, hand shape, handwritten signature, and voice. Unfortunately, from the human factors point of view, these systems are highly invasive. One possible alternative to these methods that has the potential to be less invasive is automated iris recognition. Interestingly, the spatial patterns that are apparent in the human iris are highly distinctive to an individual. The iris has unique features and is complex enough to be used as a biometric signature. Therefore, in order to use the iris pattern for identification, it is important to define a representation that is well adapted for extracting the iris information content from images of the human eye. Here we represent a new algorithm for extracting unique features from images of the iris of the human eye and representing these features using two-dimensional dual-tree complex wavelet transform (DTCWT). This representation is then utilized to recognize individuals from images of the irises of their eyes. The proposed technique is translation & shift invariant. For the dual filter tree, we have selected two linear phase biorthogonal filter sets of same lengths (based on Selesnick’s approach) which are used to filter each signal for quantization to 375 byte iris feature codes. Then the Hamming distance is used to match two iris codes. The experimental results on UPOL database shows good reliability and performance, so it is promising to be used in a personal identification system.
Keywords: Biometrics, Complex Wavelet Transform, Feature extraction, Hamming distance.