Multi-descriptor random sampling for patch-based face recognition

Cheheb, I. ORCID: 0000-0002-0961-0476, Al-Maadeed, N., Bouridane, A., Beghdadi, A. and Jiang, R., 2021. Multi-descriptor random sampling for patch-based face recognition. Applied Sciences, 11 (14): 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: Journal article
Publication Title: Applied Sciences
Creators: Cheheb, I., Al-Maadeed, N., Bouridane, A., Beghdadi, A. and Jiang, R.
Publisher: MDPI AG
Date: 2021
Volume: 11
Number: 14
ISSN: 2076-3417
Rights: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 11 Apr 2022 09:01
Last Modified: 11 Apr 2022 09:01

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