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Face Recognition Using Dense SIFT Feature Alignment
Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
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2016 (English)In: Chinese journal of electronics, ISSN 1022-4653, E-ISSN 2075-5597, Vol. 25, no 6, 1034-1039 p.Article in journal (Refereed) Published
Abstract [en]

This paper addresses face recognition problem in a more challenging scenario where the training and test samples are both subject to the visual variations of poses, expressions and misalignments. We employ dense Scale-invariant feature transform (SIFT) feature matching as a generic transformation to roughly align training samples; and then identify input facial images via an improved sparse representation model based on the aligned training samples. Compared with previous methods, the extensive experimental results demonstrate the effectiveness of our method for the task of face recognition on three benchmark datasets.

Place, publisher, year, edition, pages
2016. Vol. 25, no 6, 1034-1039 p.
Keyword [en]
Face recognition, Dense SIFT feature alignment, Sparse representation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:umu:diva-129909DOI: 10.1049/cje.2016.10.001ISI: 000387735900007OAI: oai:DiVA.org:umu-129909DiVA: diva2:1064770
Available from: 2017-01-13 Created: 2017-01-10 Last updated: 2017-01-13Bibliographically approved

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Rehman, Shafiq Ur
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Department of Applied Physics and Electronics
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CiteExportLink to record
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