Random sampling for patch-based face recognition

Cheheb, Ismahane, Al-Maadeed, Noor, Al-Madeed, Somaya, Bouridane, Ahmed and Jiang, Richard (2017) Random sampling for patch-based face recognition. In: IWBF 2017 - 5th International Workshop on Biometrics and Forensics, 4th - 5th April 2017, Coventry, UK.

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Cheheb et al - Random sampling for patch-based face recognition.pdf - Accepted Version

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Official URL: https://doi.org/10.1109/IWBF.2017.7935104

Abstract

Real face recognition is a challenging problem especially when face images are subject to distortions. This paper presents an approach to tackle partial occlusion distortions present in real face recognition using a single training sample per person. First, original images are partitioned into multiple blocks and Local Binary Patterns are applied as a local descriptor on each block separately. Then, a dimensionality reduction of the resulting descriptors is carried out using Kernel Principle Component Analysis. Once done, a random sampling method is used to select patches at random and hence build several sub-SVM classifiers. Finally, the results from each sub-classifier are combined in order to increase the recognition performance. To demonstrate the usefulness of the approach, experiments were carried on the AR Face Database and obtained results have shown the effectiveness of our technique.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Face, Face recognition, Kernel, Principal component analysis, Feature extraction, Support vector machines, Histograms
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Paul Burns
Date Deposited: 28 Jul 2017 15:14
Last Modified: 07 Sep 2017 09:33
URI: http://nrl.northumbria.ac.uk/id/eprint/31424

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