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- W2391604475 abstract "A new frontal face recognition method combining multi-scale gradient angular(MSGA) feature and support vector machine(SVM) was proposed in order to improve the performance of the face recognition algorithm.Based on gradient's insensitiveness to the illumination variation and the derivative feature of anti-symmetrical biorthogonal wavelet(ASBW),multi-scale gradient angle features were obtained.This MSGA based method is robust due to making use of the ability to eliminate the influences arising from noise,expression change and other factors,while SVM promotes the generalization ability.The recognition results on Yale face database were compared with the normalized original data and those of the wavelet transformation.Whether principal component analysis(PCA) or linear discriminant analysis(LDA) was used for dimensionality reduction,this method outperformed the others under the same dimension." @default.
- W2391604475 created "2016-06-24" @default.
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- W2391604475 date "2008-01-01" @default.
- W2391604475 modified "2023-09-23" @default.
- W2391604475 title "Frontal face recognition method based on multi-scale gradient angular feature and support vector machine" @default.
- W2391604475 hasPublicationYear "2008" @default.
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