An Iris Recognition and Detection System Implementation
Olatunbosun A.1, Bamigboye O2
1Olatunbosun A., Department of Electrical and Electronic Engineering, University of Ibadan, Nigeria.
2Bamigboye O, Department of Electrical and Electronic Engineering, Federal Polytechnic OFFA, Kwara State.
Manuscript received on February 13, 2020. | Revised Manuscript Received on February 20, 2020. | Manuscript published on February 20, 2020. | PP: 8-10 | Volume-5 Issue-8, February 2020. | Retrieval Number: H0958035820/2020©LSP | DOI: 10.35940/ijies.H0958.025820
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© The Authors. Published By: 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: There was big interest in the use of biometrical identification techniques, such as iris, face, fingerprints, ears, and significant technological developments and to enhance safety issues. The application varies depending on the location, based on the resources.They are used in safety at airports, border safety, criminal investigation, and so on. This study focuses on iris-based biometric technology. Biometric technology based on the iris diaphragm is the most reliable and acceptable among other biometric technologies.In this study, we developed the IRIS graphic user interface and attempted to use the streamlined segmentation technique to create a simpler and efficient way to detect iris. The ‘ Matlab ‘ software tool is being used to fix the recognized issues when implementing the produced code using suitable new algorithms. The proposed system is not just used to eliminates noises but also enables the border between the iris and the pupil to be correctly established. Results are saved in a computer with the corresponding model steps are performed using neural networks and synthesis algorithms. Pattern compatibility uses the appropriate metric to compare custom patterns with database patterns. The match option shows the measure of similarity between two diaphragm patterns. Finally, it is a strong level of trust that determines whether the user is authenticated or defined. As a binary template referred to as iris code the output of the Gabor wavelet (real and imaginary) is quantized as a stage. The FAR and FRR resulting from that are 0.001% and 37,880%.
Keywords: Recognition, FRR and FAR, IRIS, Gabor and Detection.