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Face recognition using decimated redundant discrete wavelet transforms
Li DQ(李德强); Tang XS(唐旭晟); Pedrycz, Witold
Department光电信息技术研究室
Source PublicationMACHINE VISION AND APPLICATIONS
ISSN0932-8092
2012
Volume23Issue:2Pages:391-401
Indexed BySCI ; EI
EI Accession number20122315085978
WOS IDWOS:000302707100013
Contribution Rank1
Funding OrganizationThe authors would like to thank Yongguo Wu, Dr. Shiguang Shan and Dr. Xiaojun Qi for their helps in improving the paper, and thank the anonymous reviewers for their critical and constructive comments and suggestions. This work was supported by the National Natural Science Foundation of China under contract 60603097.
KeywordRedundant Discrete Wavelet Transform Decimated Redundant Discrete Wavelet Transform Face Recognition Misalignment
AbstractAs discrete wavelet transform (DWT) is sensitive to the translation/shift of input signals, its effectiveness could be lessened for face recognition, particularly when the face images are translated. To alleviate drawbacks resulted from this translation effect, we propose a decimated redundant DWT (DRDWT)-based face recognition method, where the decimation-based DWTs are performed on the original signal and its 1-stepshift, respectively. Even though the DRDWT realizes the decimation, it enables us to explore the translation invariant DWT representation for the periodic shifts of the probe image that is the most similar to the gallery images. Therefore, it can solve the problem of translation sensitivity of the original DWT and address the translation effect occurring between the probe image and the gallery image. To further improve the recognition performance, we combine the global wavelet features obtained from the entire face and the local wavelet features obtained from face patches to represent both holistic and detail facial features, apply separate classifiers to global and local features and combine the resulted global and local classifiers to form an ensemble classifier. Experimental results reported for the FERET and FRGCv2.0 databases show the effectiveness of the DRDWT method and quantify its performance.
Language英语
WOS HeadingsScience & Technology ; Technology
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Engineering, Electrical & Electronic
WOS KeywordFEATURE-EXTRACTION ; GRAND CHALLENGE ; CLASSIFICATION ; REPRESENTATION ; CLASSIFIERS ; ALGORITHMS ; FEATURES ; NOISE
WOS Research AreaComputer Science ; Engineering
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/10064
Collection光电信息技术研究室
Corresponding AuthorLi DQ(李德强); Pedrycz, Witold
Affiliation1.Key Laboratory of Opto-Electronic Information Processing, CAS, Key Laboratory of Image Understanding and Computer Vision, Shenyang Institute of Automation, Shenyang 110015, China
2.Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 2V4, Canada
3.Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland
Recommended Citation
GB/T 7714
Li DQ,Tang XS,Pedrycz, Witold. Face recognition using decimated redundant discrete wavelet transforms[J]. MACHINE VISION AND APPLICATIONS,2012,23(2):391-401.
APA Li DQ,Tang XS,&Pedrycz, Witold.(2012).Face recognition using decimated redundant discrete wavelet transforms.MACHINE VISION AND APPLICATIONS,23(2),391-401.
MLA Li DQ,et al."Face recognition using decimated redundant discrete wavelet transforms".MACHINE VISION AND APPLICATIONS 23.2(2012):391-401.
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