Performance Analysis of Iris Recognition Using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique
Sunil Swamilingappa Harakannanavar1, Prashanth C R2, Raja K B3
1Sunil Swamilingappa Harakannanavar, Department of Electronics and Communication Engineering, S. G. Balekundri Institute of Technology, Belagavi, Karnataka, India.
2Prashanth C R, Department of Telecommunication Engineering, Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India.
3Raja K B, Department of Electronics and Communication Engineering, University Visvesvaraya College of Engineering, Bangalore, Karnataka, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1-10 | Volume-8 Issue-6, August 2019. | Retrieval Number: E7322068519/2019©BEIESP | DOI: 10.35940/ijeat.E7322.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Biometric identification is highly reliable for human identification. Biometric is a field of science used for analyzing the physiological or behavioural characteristics of human. Iris features are unique, stable and can be visible from longer distances. It uses mathematical pattern-recognition techniques on video images of one or both iris of an individual’s. Compared to other biometric traits, iris is more challenging and highly secured tool to identify the individual. In this paper iris recognition based on the combination of Discrete Wavelet Transform (DWT), Inverse Discrete Wavelet Transform (IDWT), Independent Component Analysis (ICA) and Binariezed Statistical Image Features (BSIF) are adopted to generate the hybrid iris features. The first level and second level DWT are employed in order to extract the more unique features of the iris images. The concept of bicubic interpolation is applied on high frequency coefficients generated by first level decomposition of DWT to produce new set of sub-bands. The approximation band generated by second level decomposition of DWT and new set of sub-bands produced by second level decomposition of DWT are applied on IDWT to reconstruct the coefficients. The ICA 5×5 filters and BSIF are adopted for selecting the appropriate images to extract the final features. Finally based on the matching score between the database image and test image the genuine and imposters are identified. Using CASIA database, training and testing of the features is performed and performance is evaluated considering different combinations of Person inside Database (PID) and Person outside Database (POD).
Keywords: Biometrics, Discrete Wavelet Transform, Independent Component Analysis, Binarized Statistical Image Features, Inverse Discrete Wavelet Transform.