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Neural Computing and Applications

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01.10.2015 | Original Article

Low-resolution degradation face recognition over long distance based on CCA

Erschienen in: Neural Computing and Applications | Ausgabe 7/2015

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Abstract

Canonical correlation analysis (CCA) is a kind of classical multivariate analysis method. Less canonical correlation variables are used to describe the relationship between two variables completely but easily. To get high face recognition rate under low-resolution degradation over a long distance solidly, in this work, CCA is used to extract the correlation between high-resolution face images and low-resolution ones and to find the transform pair between them. Therefore, face images of the same individual with variable resolutions can be matched accurately. This is the first method that uses CCA to do low-resolution degradation face recognition over long distances. We conduct the experiments on the Extended Yale B and ORL database, and the experimental results validate the efficacy of the proposed method.

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Titel: Low-resolution degradation face recognition over long distance based on CCA
Publikationsdatum: 01.10.2015
Erschienen in: Neural Computing and Applications / Ausgabe 7/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-015-1834-y

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