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Robust Face Recognition Under Partial Occlusion Based on Local Generic Features

Robust Face Recognition Under Partial Occlusion Based on Local Generic Features

Amit Kumar Yadav, Neeraj Gupta, Aamir Khan, Anand Singh Jalal
Copyright: © 2021 |Volume: 15 |Issue: 3 |Pages: 11
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859840|DOI: 10.4018/IJCINI.20210701.oa4
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MLA

Yadav, Amit Kumar, et al. "Robust Face Recognition Under Partial Occlusion Based on Local Generic Features." IJCINI vol.15, no.3 2021: pp.47-57. http://doi.org/10.4018/IJCINI.20210701.oa4

APA

Yadav, A. K., Gupta, N., Khan, A., & Jalal, A. S. (2021). Robust Face Recognition Under Partial Occlusion Based on Local Generic Features. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(3), 47-57. http://doi.org/10.4018/IJCINI.20210701.oa4

Chicago

Yadav, Amit Kumar, et al. "Robust Face Recognition Under Partial Occlusion Based on Local Generic Features," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.3: 47-57. http://doi.org/10.4018/IJCINI.20210701.oa4

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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.