Multi-Descriptor Random Sampling for Patch-Based Face Recognition

Cheheb, Ismahane, Al-Maadeed, Noor, Bouridane, Ahmed, Beghdadi, Azeddine and Jiang, Richard (2021) Multi-Descriptor Random Sampling for Patch-Based Face Recognition. Applied Sciences, 11 (14). p. 6303. ISSN 2076-3417

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While there has been a massive increase in research into face recognition, it remains a challenging problem due to conditions present in real life. This paper focuses on the inherently present issue of partial occlusion distortions in real face recognition applications. We propose an approach to tackle this problem. First, face images are divided into multiple patches before local descriptors of Local Binary Patterns and Histograms of Oriented Gradients are applied on each patch. Next, the resulting histograms are concatenated, and their dimensionality is then reduced using Kernel Principle Component Analysis. Once completed, patches are randomly selected using the concept of random sampling to finally construct several sub-Support Vector Machine classifiers. The results obtained from these sub-classifiers are combined to generate the final recognition outcome. Experimental results based on the AR face database and the Extended Yale B database show the effectiveness of our proposed technique.

Item Type: Article
Additional Information: Funding information: This research was funded by NPRP grant # NPR 8-140-2-065 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Uncontrolled Keywords: face recognition; random sampling; SVM classification
Subjects: G400 Computer Science
G500 Information Systems
G600 Software Engineering
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 08 Jul 2021 10:58
Last Modified: 31 Jul 2021 10:18

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