Enhancing Face Recognition Performance using Triplet Half Band Wavelet Filter Bank
Автор: Mohd.Abdul Muqeet, Raghunath S.Holambe
Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp
Статья в выпуске: 12 vol.8, 2016 года.
Бесплатный доступ
Face recognition using subspace methods are quite popular in research community. This paper proposes an efficient face recognition method based on the application of recently developed triplet half band wavelet filter bank (TWFB) as pre-processing step to further enhance the performance of well known linear and nonlinear subspace methods such as principle component analysis(PCA),kernel principle component analysis (KPCA), linear discriminant analysis (LDA), and kernel discriminant analysis (KDA). The design of 6th order TWFB is used as the multiresolution analysis tool to perform the 2-D discrete wavelet transform (DWT). Experimental results are performed on two standard databases ORL and Yale. Comparative results are obtained in terms of verification performance parameters such as false acceptance rate (FAR), false rejection rate (FRR) and genuine acceptance rate (GAR). Application of TWFB enhances the performance of PCA, KPCA, LDA, and KDA based methods.
Face Recognition, triplet half band wavelet filter bank (TWFB), PCA, KPCA, LDA, KDA
Короткий адрес: https://sciup.org/15014101
IDR: 15014101
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