Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/95583
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | en_US |
dc.creator | Shakeel, MS | en_US |
dc.creator | Lam, KM | en_US |
dc.creator | Lai, SC | en_US |
dc.date.accessioned | 2022-09-22T06:13:58Z | - |
dc.date.available | 2022-09-22T06:13:58Z | - |
dc.identifier.issn | 1047-3203 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/95583 | - |
dc.language.iso | en | en_US |
dc.publisher | Academic Press | en_US |
dc.rights | © 2019 Elsevier Inc. All rights reserved. | en_US |
dc.rights | © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Shakeel, M. S., Lam, K. M., & Lai, S. C. (2019). Learning sparse discriminant low-rank features for low-resolution face recognition. Journal of Visual Communication and Image Representation, 63, 102590 is available at https://doi.org/10.1016/j.jvcir.2019.102590. | en_US |
dc.subject | Face recognition | en_US |
dc.subject | Feature fusion | en_US |
dc.subject | Linear regression | en_US |
dc.subject | Local features | en_US |
dc.subject | Low rank approximation | en_US |
dc.subject | Sparse coding | en_US |
dc.title | Learning sparse discriminant low-rank features for low-resolution face recognition | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 63 | en_US |
dc.identifier.doi | 10.1016/j.jvcir.2019.102590 | en_US |
dcterms.abstract | In this paper, we propose a novel approach for low-resolution face recognition, under uncontrolled settings. Our approach first decomposes a multiple of extracted local features into a set of representative basis (low-rank matrix) and sparse error matrix, and then learns a projection matrix based on our proposed sparse-coding-based algorithm, which preserves the sparse structure of the learned low-rank features, in a low-dimensional feature subspace. Then, a coefficient vector, based on linear regression, is computed to determine the similarity between the projected gallery and query image's features. Furthermore, a new morphological pre-processing approach is proposed to improve the visual quality of images. Our experiments were conducted on five available face-recognition datasets, which contain images with variations in pose, facial expressions and illumination conditions. Experiment results show that our method outperforms other state-of–the-art low-resolution face recognition methods in terms of recognition accuracy. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of visual communication and image representation, Aug. 2019, v. 63, 102590 | en_US |
dcterms.isPartOf | Journal of visual communication and image representation | en_US |
dcterms.issued | 2019-09 | - |
dc.identifier.scopus | 2-s2.0-85070200811 | - |
dc.identifier.artn | 102590 | en_US |
dc.description.validate | 202209_bcww | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0338 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 20082765 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Shakeel_Learning_Sparse_Discriminant.pdf | Pre-Published version | 2.49 MB | Adobe PDF | View/Open |
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