Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.

The Changing Face of Neolithic and Bronze Age Ireland: A Big Data Approach to the Settlement and Burial Records

This paper synthesizes and discusses the spatial and temporal patterns of archaeological sites in Ireland, spanning the Neolithic period and the Bronze Age transition (4300–1900 cal BC), in order to explore the timing and implications of the main changes that occurred in the archaeological record of that period. Large amounts of new data are sourced from unpublished developer-led excavations and combined with national archives, published excavations and online databases. Bayesian radiocarbon models and context- and sample-sensitive summed radiocarbon probabilities are used to examine the dataset. The study captures the scale and timing of the initial expansion of Early Neolithic settlement and the ensuing attenuation of all such activity—an apparent boom-and-bust cycle. The Late Neolithic and Chalcolithic periods are characterised by a resurgence and diversification of activity. Contextualisation and spatial analysis of radiocarbon data reveals finer-scale patterning than is usually possible with summed-probability approaches: the boom-and-bust models of prehistoric populations may, in fact, be a misinterpretation of more subtle demographic changes occurring at the same time as cultural change and attendant differences in the archaeological record.

Face recognition using a unified 3D morphable model

We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rather than 3D capture. This is accomplished by modelling the difference in the texture map of the 3D aligned input and reference images. A training set of these texture maps then defines a perturbation space which can be represented using PCA bases. Assuming that the image perturbation subspace is orthogonal to the 3D face model space, then these additive components can be recovered from an unseen input image, resulting in an improved fit of the 3D face model. The linearity of the model leads to efficient fitting. Experiments show that our method achieves very competitive face recognition performance on Multi-PIE and AR databases. We also present baseline face recognition results on a new data set exhibiting combined pose and illumination variations as well as occlusion.

LtpD is a novel legionella pneumophila effector that binds phosphatidylinositol 3-phosphate and inositol monophosphatase IMPA1

The Dot/Icm type IV secretion system (T4SS) of Legionella pneumophila is crucial for the pathogen to survive in protozoa and cause human disease. Although more than 275 effector proteins are delivered into the host cell by the T4SS, the function of the majority is unknown. Here we have characterized the Dot/Icm effector LtpD. During infection, LtpD localized to the cytoplasmic face of the membrane of the Legionella-containing vacuole (LCV). In A549 lung epithelial cells, ectopically expressed LtpD localized to large vesicular structures that contained markers of endosomal compartments. Systematic analysis of LtpD fragments identified an internal 17-kDa fragment, LtpD_471-626, which was essential for targeting ectopically expressed LtpD to vesicular structures and for the association of translocated LtpD with the LCV. LtpD_471-626 bound directly to phosphatidylinositol 3-phosphate [PtdIns(3)P] in vitro and colocalized with the PtdIns(3)P markers FYVE and SetA in cotransfected cells. LtpD was also found to bind the host cell enzyme inositol (myo)-1 (or 4)-monophosphatase 1, an important phosphatase involved in phosphoinositide production. Analysis of the role of LtpD in infection showed that LtpD is involved in bacterial replication in THP-1 macrophages, the larvae of Galleria mellonella, and mouse lungs. Together, these data suggest that LtpD is a novel phosphoinositide- binding L. pneumophila effector that has a role in intracellular bacterial replication. © 2013, American Society for Microbiology.