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Luminescent Eu-containing copolymers were synthesized through the copolymerization of Eu−complex monomers containing β-diketones with methyl methacrylate and characterized by FT-IR, UV−vis, XPS, GPC, TGA, and DSC. All these Eu copolymers are fully soluble in common organic solvents and can be cast into transparent, uniform, thin films with good mechanical flexibility and thermal stability. The Eu−copolymer luminophores exhibited intense red light at 615 nm under UV excitation at room temperature, which is attributed to the 5D0→7F2 transition of Eu(III) ions. The luminescence intensities, lifetimes and monochromaticities, η(5D0→7F2/5D0→7F1), of the Eu−copolymers are much higher than that of the corresponding Eu−complex monomers as well as Eu−complex/PMMA blends. Energy transfer processes and microenvironment effects were found to contribute to the improvement of the luminescence properties of the Eu−copolymers. A study of the dependence of emission intensities of the Eu−copolymers on the Eu content showed that the emission intensities increased nearly linearly with increasing Eu content, and no significant emission concentration quenching phenomenon was observed at the Eu content of 0−6.38 mol %. This indicates that the Eu−complex units in the Eu−copolymers are very uniformly bonded to the polymer chain, which is in good agreement with the result of polymer structure analysis.

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To address the problem of face recognition with image sets, we aim to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as the Gaussian mixture model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. Since in the light of information geometry, the Gaussians lie on a specific Riemannian manifold, this paper presents a method named discriminant analysis on Riemannian manifold of Gaussian distributions (DARG). We investigate several distance metrics between Gaussians and accordingly two discriminative learning frameworks are presented to meet the geometric and statistical characteristics of the specific manifold. The first framework derives a series of provably positive definite probabilistic kernels to embed the manifold to a high-dimensional Hilbert space, where conventional discriminant analysis methods developed in Euclidean space can be applied, and a weighted Kernel discriminant analysis is devised which learns discriminative representation of the Gaussian components in GMMs with their prior probabilities as sample weights. Alternatively, the other framework extends the classical graph embedding method to the manifold by utilizing the distance metrics between Gaussians to construct the adjacency graph, and hence the original manifold is embedded to a lower-dimensional and discriminative target manifold with the geometric structure preserved and the interclass separability maximized. The proposed method is evaluated by face identification and verification tasks on four most challenging and largest databases, YouTube Celebrities, COX, YouTube Face DB, and Point-and-Shoot Challenge, to demonstrate its superiority over the state-of-the-art.To address the problem of face recognition with image sets, we aim to capture the underlying data distribution in each set and thus facilitate more robust classification. To this end, we represent image set as the Gaussian mixture model (GMM) comprising a number of Gaussian components with prior probabilities and seek to discriminate Gaussian components from different classes. Since in the light of information geometry, the Gaussians lie on a specific Riemannian manifold, this paper presents a method named discriminant analysis on Riemannian manifold of Gaussian distributions (DARG). We investigate several distance metrics between Gaussians and accordingly two discriminative learning frameworks are presented to meet the geometric and statistical characteristics of the specific manifold. The first framework derives a series of provably positive definite probabilistic kernels to embed the manifold to a high-dimensional Hilbert space, where conventional discriminant analysis methods developed in Euclidean space can be applied, and a weighted Kernel discriminant analysis is devised which learns discriminative representation of the Gaussian components in GMMs with their prior probabilities as sample weights...