Biometric Iris Recognition of Person from an Image at Long Distance using Chronological Monarch Butterfly Optimization based Deep Belief Network
Swati D. Shirke1, C. Rajabhushanam2
1Ms. Swati D. Shirke, Ph.D Scholor, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
2Dr. C. Rajabhushanam, Professor, Department of Computer Science & Engineering, Bharath Institute of Higher Education and Research, Chennai (Tamil Nadu), India.
Manuscript received on 25 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1975-1983 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B15060882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1506.0882S819
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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: Now days, for the identification of personal information of a person, biometrics is mostly used. Also for the personal identification, the recognition of eye based biometric feature extraction is the most powerful tool. The biometric is an important identity to identify the individual. But in real time it is quite difficult to capture the better quality of iris images. The images obtained are more degraded due to the lack of texture, blur. In this paper, more convenient method is presented for extracting the features of biometric images. The method Iris Recognition at-a Distance (IAAD) is used to extract the iris features of biometric image and to enhance the quality of an image in a biometric system. The Chronological Monarch Butterfly Optimization -based Deep Belief Network (Chronological MBO-based DBN) is proposed for iris recognition to get better accuracy. The Monarch Butterfly Optimization algorithm is used to arrange the Chronological assumption of an iris image. Also, the Hough Transform algorithm is used for detection of iris circle and edge. The scaT T loop descriptor and the Local Gradient Pattern (LGP) are used for feature extraction, which is fed to the Chronological MBO-based DBN for iris recognition that enhances the accuracy. The Daugman’s rubber sheet model, median filter and trained neural network are used for normalization and segmentation. The UBIRIS.v1 database is used to take an iris recognition images and MATLAB is used for programming of for reading the iris images and for performing the Hough transform operations. The iris recognition at a distance 4 to 8 meter is done with the help of simulation result. The performance is analyzed based on the metrics, like False Acceptance Rate (FAR), accuracy, and False Rejection Rate (FRR) with the value of 0.4847%, 96.078%, and 0.4745%.
Keywords: Deep Belief Network, Matlab, Iris Recognition, Hough Transform, ScatT-Loop, LGP, Feature Extraction, Dougman’s Rubber Sheet Model, etc.
Scope of the Article: Deep Learning