Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform
Result description
This study proposes a novel near infrared face recognition algorithm based on a combination of both local and global features. In this method local features are extracted from partitioned images by means of undecimated discrete wavelet transform (UDWT) and global features are extracted from the whole face image by means of Zernike moments (ZMs). Spectral regression discriminant analysis (SRDA) is then used to reduce the dimension of features. In order to make full use of global and local features and further improve the performance, a decision fusion technique is employed by using weighted sum rule. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that the proposed method has superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments. Moreover its computational time is acceptable for on-line face recognition systems.
Keywords
Zernike momentsUndecimated discrete wavelet transformDecision fusionNear infraredFace recognition
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform
Original language description
This study proposes a novel near infrared face recognition algorithm based on a combination of both local and global features. In this method local features are extracted from partitioned images by means of undecimated discrete wavelet transform (UDWT) and global features are extracted from the whole face image by means of Zernike moments (ZMs). Spectral regression discriminant analysis (SRDA) is then used to reduce the dimension of features. In order to make full use of global and local features and further improve the performance, a decision fusion technique is employed by using weighted sum rule. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that the proposed method has superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments. Moreover its computational time is acceptable for on-line face recognition systems.
Czech name
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Czech description
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Classification
Type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
GAP103/11/1552: Moments and Moment Invariants in Image Analysis
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Digital Signal Processing
ISSN
1051-2004
e-ISSN
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Volume of the periodical
31
Issue of the periodical within the volume
1
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
27
Pages from-to
13-27
UT code for WoS article
000337267200002
EID of the result in the Scopus database
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Result type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP
JD - Use of computers, robotics and its application
Year of implementation
2014