Plasmonic nanorod metamaterials exhibit transversal and longitudinal resonance modes. It is found that the resonance intensity of the transversal modes (T-Modes) excited by the p- polarized wave is obviously larger than the intensity for the s- polarized wave at the wavelength of the transversal resonance, and the resonance intensity of the longitudinal modes (L-Modes) excited by the s- polarized wave is clearly larger than the intensity for the p- polarized wave at the longitudinal resonance wavelength, indicating a distinct polarization characteristics, which results from excitation of the different resonance modes of surface plasmons at different wavelengths. Moreover, the polarization behavior in near field regions for the different resonance modes has been demonstrated by the electric field distributions of the plasmonic nanorods based on FDTD simulation. In addition, the working wavelength of the polarizer can be tuned by the diameter and length of the silver nanorods in the visible spectral range, higher extinction ratios and lower insertion losses can be achieved based on the different resonance modes associated with the different polarizations. The polarizers will be a promising candidate for its potential applications in integration of nanophotonic devices.
Objective: Face Recognition is one of the widely application of image processing. Corresponding two-dimensional limitations, such as the pose and illumination changes, to a certain extent restricted its accurate rate and further development. How to overcome the pose and illumination changes and the effects of self-occlusion is the research hotspot and difficulty, also attracting more and more domestic and foreign experts and scholars to study it. 3D face recognition fusing shape and texture descriptors has become a very promising research direction. Method: Our paper presents a 3D point cloud based on mesh local binary pattern grid (Mesh-LBP), then feature extraction for 3D face recognition by fusing shape and texture descriptors. 3D Mesh-LBP not only retains the integrity of the 3D geometry, is also reduces the need for recognition process of normalization steps, because the triangle Mesh-LBP descriptor is calculated on 3D grid. On the other hand, in view of multi-modal consistency in face recognition advantage, construction of LBP can fusing shape and texture information on Triangular Mesh. In this paper, some of the operators used to extract Mesh-LBP, Such as the normal vectors of the triangle each face and vertex, the gaussian curvature, the mean curvature, laplace operator and so on. Conclusion: First, Kinect devices obtain 3D point cloud face, after the pretreatment and normalization, then transform it into triangular grid, grid local binary pattern feature extraction from face key significant parts of face. For each local face, calculate its Mesh-LBP feature with Gaussian curvature, mean curvature laplace operator and so on. Experiments on the our research database, change the method is robust and high recognition accuracy.