A New Enhanced Template Protection Algorithm on Iris Recognition
Monis Khan1, Suraj Yadav2
1Monis Khan*, Department of Computer Science and Engineering, Jagannath University, Jaipur, India.
2Suraj Yadav, Department of Computer Science and Engineering, Jagannath University, Jaipur, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 30, 2020. | PP: 286-289 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7208038620 /2020©BEIESP | DOI: 10.35940/ijrte.F7208.038620
Open Access | Ethics and Policies | Cite | Mendeley
© 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: Over the past few years, biometric systems have become prominent in terms of verification of the user identity due to increased demand of security in the networked society. Iris recognition system is a novel technology for the verification of user which is considered as the most secure, reliable and stable technique. It is generally accepted in the areas with high security. Though, security is major concern in this field, a significant number of approaches have been proposed to secure iris biometrics, But still, there is a scope to improve these techniques. Thus, in this work, a novel model is proposed which employs a bitmask compression technique to secure the template obtained for iris by compressing its actual size. In addition; SVM is used for the classification process. Mean Square Error, Bit Error Rate, PSNR, and GAR are different parameters which are used for measuring the effectiveness of the proposed model. The simulation results are carried out in MATLAB software and the comparative results validated the efficacy of the novel model with respect to security, efficacy and accuracy.
Keywords: Iris recognition, Bitmask compression technique, Support Vector Machines (SVMs) technique.
Scope of the Article: Support Vector Machines