Multi-Descriptor Random Sampling for Patch-Based Face Recognition

Ismahane Cheheb*, Noor Al-Maadeed, Ahmed Bouridane, Azeddine Beghdadi, Richard Jiang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Downloads (Pure)

Abstract

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.
Original languageEnglish
Article number6303
Number of pages14
JournalApplied Sciences
Volume11
Issue number14
DOIs
Publication statusPublished - 8 Jul 2021

Fingerprint

Dive into the research topics of 'Multi-Descriptor Random Sampling for Patch-Based Face Recognition'. Together they form a unique fingerprint.

Cite this