Face annotation technology is important for a photo management system. In this paper, we propose a novel interactive face annotation framework combining unsupervised and interactive learning. There are two main contributions in our framework. In the unsupervised stage, a partial clustering algorithm is proposed to find the most evident clusters instead of grouping all instances into clusters, which leads to a good initial labeling for later user interaction. In the interactive stage, an efficient labeling procedure based on minimization of both global system uncertainty and estimated number of user operations is proposed to reduce user interaction as much as possible. Experimental results show that the proposed annotation framework can significantly reduce the face annotation workload and is superior to existing solutions in the literature.

Linear discriminant analysis (LDA) is a popular face recognition technique. However, an inherent problem with this technique stems from the parametric nature of the scatter matrix, in which the sample distribution in each class is assumed to be normal distribution. So it tends to suffer in the case of non-normal distribution. In this paper a nonparametric scatter matrix is defined to replace the traditional parametric scatter matrix in order to overcome this problem. Two kinds of nonparametric subspace analysis (NSA): PNSA and NNSA are proposed for face recognition. The former is based on the principal space of intra-personal scatter matrix, while the latter is based on the null space. In addition, based on the complementary nature of PNSA and NNSA, we further develop a dual NSA-based classifier framework using Gabor images to further improve the recognition performance. Experiments achieve near perfect recognition accuracy (99.7%) on the XM2VTS database.

In this paper, we propose a new face hallucination framework based on image patches, which integrates two novel statistical super-resolution models. Considering that image patches reflect the combined effect of personal characteristics and patch-location, we first formulate a TensorPatch model based on multilinear analysis to explicitly model the interaction between multiple constituent factors. Motivated by locally linear embedding, we develop an enhanced multilinear patch hallucination algorithm, which efficiently exploits the local distribution structure in the sample space. To better preserve face subtle details, we derive the coupled PCA algorithm to learn the relation between high-resolution residue and low-resolution residue, which is utilized for compensate the error residue in hallucinated images. Experiments demonstrate that our framework on one hand well maintains the global facial structures, on the other hand recovers the detailed facial traits in high quality.

In this paper, we propose a novel framework for face super-resolution based on a layered predictor network. In the first layer, multiple predictors are trained online with a dynamic-constructed training set, which is adaptively selected in order to make the trained model tailored to the testing face. When the dynamic training set is obtained, the optimum predictor can be learned based on the resampling-maximum likelihood-model. To further enhance the robustness of prediction and the smoothness of the hallucinated image, additional layers are designed to fuse multiple predictors with the fusion rule learned from the training set. Experiments fully demonstrate the effectiveness of the framework.

In this paper, we propose a novel patch-based face hallucination framework, which employs a dual model to hallucinate different components associated with one facial image. Our model is based on a statistical learning approach: associative learning. It suffices to learn the dependencies between low-resolution image patches and their high-resolution ones with a new concept hidden parameter space as a bridge to connect those patches with different resolutions. To compensate higher frequency information of images, we present a dual associative learning algorithm for orderly inferring main components and high frequency components of faces. The patches can be finally integrated to form a whole high-resolution image. Experiments demonstrate that our approach does render high quality superresolution faces.

In this paper, we propose a novel face hallucination framework based on image patches, which exploits local geometry structures of overlapping patches to hallucinate different components associated with one facial image. To achieve local fidelity while preserving smoothness in the target high-resolution image, we develop a neighbor combination super-resolution model for high-resolution patch synthesis. For further enhancing the detailed information, we propose another model, which effectively learns neighbor transformations between low- and high-resolution image patch residuals to compensate modeling errors caused by the first model. Experiments demonstrate that our approach can hallucinate high quality super-resolution faces.

Latent Semantic Analysis (LSA) has shown encouraging performance for the problem of unsupervised image automatic annotation. LSA conducts annotation by keywords propagation on a linear Latent Space, which accounts for the underlying semantic structure of word and image features. In this paper, we formulate a more general nonlinear model, called Nonlinear Latent Space model, to reveal the latent variables of word and visual features more precisely. Instead of the basic propagation strategy, we present a novel inference strategy for image annotation via Image-Word Embedding (IWE). IWE simultaneously embeds images and words and captures the dependencies between them from a probabilistic viewpoint. Experiments show that IWE-based annotation on the nonlinear latent space outperforms previous unsupervised annotation methods.