A face recognition scheme is proposed, wherein a face image is preprocessed by pixel averaging and energy normalizing to reduce data dimension and brightness variation effect, followed by the Fourier transform to esti...A face recognition scheme is proposed, wherein a face image is preprocessed by pixel averaging and energy normalizing to reduce data dimension and brightness variation effect, followed by the Fourier transform to estimate the spectrum of the preprocessed image. The principal component analysis is conducted on the spectra of a face image to obtain eigen features. Combining eigen features with a Parzen classifier, experiments are taken on the ORL face database.展开更多
The point spread function（PSF） is investigated in order to study the centroids algorithm in a reverse Hartmann test（RHT） system. Instead of the diffractive Airy disk in previous researches, the intensity of PSF be...The point spread function（PSF） is investigated in order to study the centroids algorithm in a reverse Hartmann test（RHT） system. Instead of the diffractive Airy disk in previous researches, the intensity of PSF behaves as a circle of confusion（CoC） and is evaluated in terms of the Lommel function in this paper. The fitting of a single spot with the Gaussian profile to identify its centroid forms the basis of the proposed centroid algorithm. In the implementation process, gray compensation is performed to obtain an intensity distribution in the form of a two-dimensional（2D） Gauss function while the center of the peak is derived as a centroid value. The segmental fringe is also fitted row by row with the one-dimensional（1D） Gauss function and reconstituted by averaged parameter values. The condition used for the proposed method is determined by the strength of linear dependence evaluated by Pearson＇s correlation coefficient between profiles of Airy disk and CoC. The accuracies of CoC fitting and centroid computation are theoretically and experimentally demonstrated by simulation and RHTs. The simulation results show that when the correlation coefficient value is more than 0.9999, the proposed centroid algorithm reduces the root-mean-square error（RMSE） by nearly one order of magnitude, thus achieving an accuracy of - 0.01 pixel or better performance in experiment. In addition, the 2D and 1D Gaussian fittings for the segmental fringe achieve almost the same centroid results, which further confirm the feasibility and advantage of the theory and method.展开更多
文摘A face recognition scheme is proposed, wherein a face image is preprocessed by pixel averaging and energy normalizing to reduce data dimension and brightness variation effect, followed by the Fourier transform to estimate the spectrum of the preprocessed image. The principal component analysis is conducted on the spectra of a face image to obtain eigen features. Combining eigen features with a Parzen classifier, experiments are taken on the ORL face database.
基金Project supported by the National Natural Science Foundation of China(Grant No.61475018)
文摘The point spread function（PSF） is investigated in order to study the centroids algorithm in a reverse Hartmann test（RHT） system. Instead of the diffractive Airy disk in previous researches, the intensity of PSF behaves as a circle of confusion（CoC） and is evaluated in terms of the Lommel function in this paper. The fitting of a single spot with the Gaussian profile to identify its centroid forms the basis of the proposed centroid algorithm. In the implementation process, gray compensation is performed to obtain an intensity distribution in the form of a two-dimensional（2D） Gauss function while the center of the peak is derived as a centroid value. The segmental fringe is also fitted row by row with the one-dimensional（1D） Gauss function and reconstituted by averaged parameter values. The condition used for the proposed method is determined by the strength of linear dependence evaluated by Pearson＇s correlation coefficient between profiles of Airy disk and CoC. The accuracies of CoC fitting and centroid computation are theoretically and experimentally demonstrated by simulation and RHTs. The simulation results show that when the correlation coefficient value is more than 0.9999, the proposed centroid algorithm reduces the root-mean-square error（RMSE） by nearly one order of magnitude, thus achieving an accuracy of - 0.01 pixel or better performance in experiment. In addition, the 2D and 1D Gaussian fittings for the segmental fringe achieve almost the same centroid results, which further confirm the feasibility and advantage of the theory and method.
