Robust Face Recognition Under Partial Occlusion Based on Local Generic Features

Amit Kumar Yadav, Neeraj Gupta, Aamir Khan and Anand Singh Jalal
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Amit Kumar Yadav: GLA University, Mathura, India
Neeraj Gupta: GLA University, Mathura, India
Aamir Khan: Independent Researcher, India
Anand Singh Jalal: GLA University, Mathura, India

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2021, vol. 15, issue 3, 47-57

Abstract: Face recognition has drawn significant attention due to its potential use in biometric authentication, surveillance, security, robotics, and so on. It is a challenging task in the field of computer vision. Although the various state-of-the-art methods of face recognition in constrained environments have achieved satisfactory results, there are still many issues which are untouched in unconstrained environments, such as partial occlusions, large pose variations, etc. In this paper, the authors have proposed an approach which utilized the local generic feature (LGF) to recognize the face in the partial occlusion by fusing features scale invariant feature transform (SIFT) and multi-block local binary pattern (MB-LBP). It also utilizes robust kernel method for classification of the query image. They have validated the effectiveness of the proposed approach on the benchmark AR face database. The experimental outcomes illustrate that the proposed approach outperformed the state-of-art methods for robust face recognition.

Date: 2021
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