Abstract
This paper presents an analysis of the recognition performance of LBP at different frequency bands to exploit their discriminative information. The work presented in this paper is part of an investigation about which aspects of a face contribute to automated face recognition. Multi-resolution analysis, by means of wavelet transform, is commonly used to explore the features of an image. The each step of wavelet transform decomposes an image recursively into four frequency bands: approximation, horizontal, vertical, and diagonal band. The approximation band is a down sampled version of the original image. Whereas, the other bands are detail bands that contain detail information of the image in horizontal, vertical, and diagonal directions. The noise is more dominant in these bands. The information contained in the detail bands is more related to high frequency-components and local structures such as edges. In order to analyze the impact of the various bands, we performed classification experiments on them. For the A-bands, that contain the global information of the facial image, we used PCA/LDA classifiers. For the detail bands, that contain local structures, we used LBP.
Original language | English |
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Title of host publication | Proceedings of the 2019 Symposium on Information Theory and Signal Processing in the Benelux |
Subtitle of host publication | May 28-29 2019, KU Leuven, Technologiecampus Gent, Belgium |
Editors | Gilles Callebaut, Kevin Verniers, Bert Cox |
Place of Publication | Leuven |
Publisher | Werkgemeenschap voor Informatie- en Communicatietheorie (WIC) |
Pages | 63-70 |
Number of pages | 8 |
ISBN (Print) | 978-94-918-5703-4 |
Publication status | Published - 28 May 2019 |
Event | 40th WIC Symposium on Information Theory in the Benelux 2019 - Ghent Technology Campus, Leuven, Belgium Duration: 28 May 2019 → 29 May 2019 Conference number: 40 |
Conference
Conference | 40th WIC Symposium on Information Theory in the Benelux 2019 |
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Country/Territory | Belgium |
City | Leuven |
Period | 28/05/19 → 29/05/19 |
Keywords
- Multi-resolution analysis
- Face recognition
- Wavelet transform
- Local binary patterns