Multi-Modal Authentication using Machine Learning Algorithm
Anil Kumar Gona1, M. Subramoniam2
1Anil Kumar Gona, Ph.D Scholar, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
2Dr. M. Subramoniam, Assistant Professor, Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 28 September 2019 | Revised Manuscript received on 10 November 2019 | Manuscript Published on 22 November 2019 | PP: 1151-1155 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F11920986S319/19©BEIESP | DOI: 10.35940/ijeat.F1192.0986S319
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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: In the present era of information technology, there is a need to implement verification and approval strategies for security of resources. Whereas, there are number of approaches to demonstrate validation and approval, yet the biometric verification beat every other method. At first, biometrics began off with straightforward unimodal framework, the higher requirement for security had offered ascend to a prevalent framework known as multimodal verification framework. Multimodal verification confirmation has pulled in compelling interest, on account of its hugeness towards the constant application In this research proposal, an effective framework for multimodal verification authentication systems based on machine learning algorithm is employed.
Keywords: The Utilized Structure Tends to the Characteristic Issues of Client Burden and Framework Wastefulness in Multimodal Confirmation Verification Frameworks.
Scope of the Article: Machine Learning