Abstract
The extraction of feature remains the significant phase in recognition system using iris. A successful recognition rate and reduction in classification time of two iris templates mostly depend on efficient feature extraction technique. This paper performs comparative analysis on two selected feature extraction techniques: Gabor Wavelet Transform (GWT) and Scale Invariant Feature Transform (SIFT). The developed system was evaluated with CASIA iris dataset. Performance evaluation of the system based on False Acceptance Rate (FAR), False Rejection Rate (FRR), Error Rate (ER) and accuracy produced different results of each technique. It was showed that the Gabor Wavelet Transform gave FAR of 0.9500, FRR of 0.0750, 92% of accuracy, and ER of 8% as compared with the SIFT technique which gave FAR of 0.900, FRR of 0.0631, ERR of 16.6% and 88.33% of accuracy. Finally, the results of comparative analysis showed that Gabor Wavelet Transform outperformed SIFT technique. From the results obtained, GWT is strongly recommended as a feature extraction method for the development of a robust iris authentication system.
Highlights
Iris recognition is a method that employs recognition using high resolution images of the humans’ irises (Khotimah & Juniati, 2018)
The level of misclassification of Scale Invariant Feature Transform (SIFT) feature-based iris recognition is higher than the Gabor Wavelet Transform
The iris feature extraction is a significant step of iris recognition system
Summary
Iris recognition is a method that employs recognition using high resolution images of the humans’ irises (Khotimah & Juniati, 2018). Human’s iris is a coloured and muscular portion within the pupil size, regulating amount of light that passes into eye (Shirke & Gupta, 2013) This part of the eye is so much distinct, that is not possible to have two individual irises look the same, even for twins that are identical (Cappelli, Maio, Maltoni, Wayman, & Jain, 2006). The Iris recognition is a dependable method that visually identify people when the imaging can be achieved at a close distances not exceeding 1 meter (Alaslani & Elrefaei, 2018). This is quite suitable in areas where a huge database will require to be searched for recognition while ensuring there are minimal or no mis-matches (Daugman, 2009). The human iris has gained attention of biometric recognition system research and development (Pithadia & Nimavat, 2015)
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