Human Face Identification based on Optimal Sparse Features
M. Risheek Sharma1, K. Akhil Vardhan2, K. Sravan Kumar3, B. Koteswarrao4, Shijin Kumar P. S.5
1M. Risheek Sharma, Department of Electronics and Communication Engineering, Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad (Telangana) India.
2K. Akhil Vardhan, Department of Electronics and Communication Engineering, Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad (Telangana) India.
3K. Sravan Kumar, Department of Electronics and Communication Engineering, Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad (Telangana) India.
4B. Koteswarrao, Department of Electronics and Communication Engineering, Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad (Telangana) India.
5Shijin Kumar P. S.*, Associate Professor, Department of Electronics and Communication Engineering, Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad (Telangana) India.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 690-693 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3098129219/2020©BEIESP | DOI: 10.35940/ijeat.B3098.129219
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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: Security of human being is an important aspect in the context of data communication. To maintain security, technology is being developed from alpha-numeric passwords to biometric scanners. Recent advancement in security is the user authentication using face recognition. But the flaws in existing face recognition systems are yet to be addressed. This paper discusses solutions to the issues encountered by face recognition systems. Sparsity based classification is performed in this work. This method can handle errors occurs due to compress in and occlusion in a robust manner. We suggest a comprehensive classification algorithm characterized by sparse representation and 1l -minimization. In this method, the feature points and selection of features are not critical. The effect of change in occlusion can be easily addressed by using this optimal sparse representation based classification (OSRC) algorithm.
Keywords: Face Detection, Sparsity, Optimal Sparse Representation based Classification, l1–minimization.