Face verification using Gabor wavelets and AdaBoost

This paper presents a new face verification algorithm based on Gabor wavelets and AdaBoost. In the algorithm, faces are represented by Gabor wavelet features generated by Gabor wavelet transform. Gabor wavelets with 5 scales and 8 orientations are chosen to form a family of Gabor wavelets. By convolving face images with these 40 Gabor wavelets, the original images are transformed into magnitude response images of Gabor wavelet features. The AdaBoost algorithm selects a small set of significant features from the pool of the Gabor wavelet features. Each feature is the basis for a weak classifier which is trained with face images taken from the XM2VTS database. The feature with the lowest classification error is selected in each iteration of the AdaBoost operation. We also address issues regarding computational costs in feature selection with AdaBoost. A support vector machine (SVM) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.

Feature-point detection using distance transforms: Application to tracking tropical convective complexes

The identification, tracking, and statistical analysis of tropical convective complexes using satellite imagery is explored in the context of identifying feature points suitable for tracking. The feature points are determined based on the shape of complexes using the distance transform technique. This approach has been applied to the determination feature points for tropical convective complexes identified in a time series of global cloud imagery. The feature points are used to track the complexes, and from the tracks statistical diagnostic fields are computed. This approach allows the nature and distribution of organized deep convection in the Tropics to be explored.

Validation of ocean model syntheses against hydrography using a new web application

Results are presented from a new web application called OceanDIVA - Ocean Data Intercomparison and Visualization Application. This tool reads hydrographic profiles and ocean model output and presents the data on either depth levels or isotherms for viewing in Google Earth, or as probability density functions (PDFs) of regional model-data misfits. As part of the CLIVAR Global Synthesis and Observations Panel, an intercomparison of water mass properties of various ocean syntheses has been undertaken using OceanDIVA. Analysis of model-data misfits reveals significant differences between the water mass properties of the syntheses, such as the ability to capture mode water properties.

Facial feature extraction and selection by Gabor wavelets and boosting

In this paper, we present a feature selection approach based on Gabor wavelet feature and boosting for face verification. By convolution with a group of Gabor wavelets, the original images are transformed into vectors of Gabor wavelet features. Then for individual person, a small set of significant features are selected by the boosting algorithm from a large set of Gabor wavelet features. The experiment results have shown that the approach successfully selects meaningful and explainable features for face verification. The experiments also suggest that for the common characteristics such as eyes, noses, mouths may not be as important as some unique characteristic when training set is small. When training set is large, the unique characteristics and the common characteristics are both important.

Proposing a hybrid-intelligent framework to secure e-government web applications

One of the essential needs to implement a successful e-Government web application is security. Web application firewalls (WAF) are the most important tool to secure web applications against the increasing number of web application attacks nowadays. WAFs work in different modes depending on the web traffic filtering approach used, such as positive security mode, negative security mode, session-based mode, or mixed modes. The proposed WAF, which is called (HiWAF), is a web application firewall that works in three modes: positive, negative and session based security modes. The new approach that distinguishes this WAF among other WAFs is that it utilizes the concepts of Artificial Intelligence (AI) instead of regular expressions or other traditional pattern matching techniques as its filtering engine. Both artificial neural networks and fuzzy logic concepts will be used to implement a hybrid intelligent web application firewall that works in three security modes.

Grid Remote Execution (G-Rex) of Climate Models

G-Rex is light-weight Java middleware that allows scientific applications deployed on remote computer systems to be launched and controlled as if they are running on the user's own computer. G-Rex is particularly suited to ocean and climate modelling applications because output from the model is transferred back to the user while the run is in progress, which prevents the accumulation of large amounts of data on the remote cluster. The G-Rex server is a RESTful Web application that runs inside a servlet container on the remote system, and the client component is a Java command line program that can easily be incorporated into existing scientific work-flow scripts. The NEMO and POLCOMS ocean models have been deployed as G-Rex services in the NERC Cluster Grid, and G-Rex is the core grid middleware in the GCEP and GCOMS e-science projects.

Running Climate Models On The NERC Cluster Grid Using G-Rex

Compute grids are used widely in many areas of environmental science, but there has been limited uptake of grid computing by the climate modelling community, partly because the characteristics of many climate models make them difficult to use with popular grid middleware systems. In particular, climate models usually produce large volumes of output data, and running them usually involves complicated workflows implemented as shell scripts. For example, NEMO (Smith et al. 2008) is a state-of-the-art ocean model that is used currently for operational ocean forecasting in France, and will soon be used in the UK for both ocean forecasting and climate modelling. On a typical modern cluster, a particular one year global ocean simulation at 1-degree resolution takes about three hours when running on 40 processors, and produces roughly 20 GB of output as 50000 separate files. 50-year simulations are common, during which the model is resubmitted as a new job after each year. Running NEMO relies on a set of complicated shell scripts and command utilities for data pre-processing and post-processing prior to job resubmission. Grid Remote Execution (G-Rex) is a pure Java grid middleware system that allows scientific applications to be deployed as Web services on remote computer systems, and then launched and controlled as if they are running on the user's own computer. Although G-Rex is general purpose middleware it has two key features that make it particularly suitable for remote execution of climate models: (1) Output from the model is transferred back to the user while the run is in progress to prevent it from accumulating on the remote system and to allow the user to monitor the model; (2) The client component is a command-line program that can easily be incorporated into existing model work-flow scripts. G-Rex has a REST (Fielding, 2000) architectural style, which allows client programs to be very simple and lightweight and allows users to interact with model runs using only a basic HTTP client (such as a Web browser or the curl utility) if they wish. This design also allows for new client interfaces to be developed in other programming languages with relatively little effort. The G-Rex server is a standard Web application that runs inside a servlet container such as Apache Tomcat and is therefore easy to install and maintain by system administrators. G-Rex is employed as the middleware for the NERC1 Cluster Grid, a small grid of HPC2 clusters belonging to collaborating NERC research institutes. Currently the NEMO (Smith et al. 2008) and POLCOMS (Holt et al, 2008) ocean models are installed, and there are plans to install the Hadley Centre’s HadCM3 model for use in the decadal climate prediction project GCEP (Haines et al., 2008). The science projects involving NEMO on the Grid have a particular focus on data assimilation (Smith et al. 2008), a technique that involves constraining model simulations with observations. The POLCOMS model will play an important part in the GCOMS project (Holt et al, 2008), which aims to simulate the world’s coastal oceans. A typical use of G-Rex by a scientist to run a climate model on the NERC Cluster Grid proceeds as follows :(1) The scientist prepares input files on his or her local machine. (2) Using information provided by the Grid’s Ganglia3 monitoring system, the scientist selects an appropriate compute resource. (3) The scientist runs the relevant workflow script on his or her local machine. This is unmodified except that calls to run the model (e.g. with “mpirun”) are simply replaced with calls to "GRexRun" (4) The G-Rex middleware automatically handles the uploading of input files to the remote resource, and the downloading of output files back to the user, including their deletion from the remote system, during the run. (5) The scientist monitors the output files, using familiar analysis and visualization tools on his or her own local machine. G-Rex is well suited to climate modelling because it addresses many of the middleware usability issues that have led to limited uptake of grid computing by climate scientists. It is a lightweight, low-impact and easy-to-install solution that is currently designed for use in relatively small grids such as the NERC Cluster Grid. A current topic of research is the use of G-Rex as an easy-to-use front-end to larger-scale Grid resources such as the UK National Grid service.

Plug-and-play remote portlet publishing

Web Services for Remote Portlets (WSRP) is gaining attention among portal developers and vendors to enable easy development, increased richness in functionality, pluggability, and flexibility of deployment. Whilst currently not supporting all WSRP functionalities, open-source portal frameworks could in future use WSRP Consumers to access remote portlets found from a WSRP Producer registry service. This implies that we need a central registry for the remote portlets and a more expressive WSRP Consumer interface to implement the remote portlet functions. This paper reports on an investigation into a new system architecture, which includes a Web Services repository, registry, and client interface. The Web Services repository holds portlets as remote resource producers. A new data structure for expressing remote portlets is found and published by populating a Universal Description, Discovery and Integration (UDDI) registry. A remote portlet publish and search engine for UDDI has also been developed. Finally, a remote portlet client interface was developed as a Web application. The client interface supports remote portlet features, as well as window status and mode functions. Copyright (c) 2007 John Wiley & Sons, Ltd.

Assessing the benefits of crop albedo bio-geoengineering

It has been proposed that growing crop varieties with higher canopy albedo would lower summer-time temperatures over North America and Eurasia and provide a partial mitigation of global warming ('bio-geoengineering') (Ridgwell et al 2009 Curr. Biol. 19 1–5). Here, we use a coupled ocean–atmosphere–vegetation model (HadCM3) with prescribed agricultural regions, to investigate to what extent the regional effectiveness of crop albedo bio-geoengineering might be influenced by a progressively warming climate as well as assessing the impacts on regional hydrological cycling and primary productivity. Consistent with previous analysis, we find that the averted warming due to increasing crop canopy albedo by 0.04 is regionally and seasonally specific, with the largest cooling of ~1 °C for Europe in summer whereas in the low latitude monsoonal SE Asian regions of high density cropland, the greatest cooling is experienced in winter. In this study we identify potentially important positive impacts of increasing crop canopy albedo on soil moisture and primary productivity in European cropland regions, due to seasonal increases in precipitation. We also find that the background climate state has an important influence on the predicted regional effectiveness of bio-geoengineering on societally-relevant timescales (ca 100 years). The degree of natural climate variability and its dependence on greenhouse forcing that are evident in our simulations highlights the difficulties faced in the detection and verification of climate mitigation in geoengineering schemes. However, despite the small global impact, regionally focused schemes such as crop albedo bio-geoengineering have detection advantages.

Weightless neural nets for face recognition: a comparison

This paper considers the application of weightless neural networks (WNNs) to the problem of face recognition and compares the results with those provided using a more complicated multiple neural network approach. WNNs have significant advantages over the more common forms of neural networks, in particular in term of speed of operation and learning. A major difficulty when applying neural networks to face recognition problems is the high degree of variability in expression, pose and facial details: the generalisation properties of a WNN can be crucial. In the light of this problem a software simulator of a WNN has been built and the results of some initial tests are presented and compared with other techniques

Stated willingness-to-pay for organic fruit and pesticide ban: an evaluation using both web-based and face-to-face interviewing

Income growth in highly industrialised countries has resulted in consumer choice of foodstuffs no longer being primarily influenced by basic factors such as price and organoleptic features. From this perspective, the present study sets out to evaluate how and to what extent consumer choice is influenced by the possible negative effects on health and environment caused by the consumption of fruit containing deposits of pesticides and chemical products. The study describes the results of a survey which explores and estimates consumer willingness to pay in two forms: a yearly contribution for the abolition of the use of pesticides on fruit, and a premium price for organically grown apples guaranteed by a certified label. The same questionnaire was administered to two samples. The first was a conventional face-to-face survey of customers of large retail outlets located around Bologna (Italy); the second was an Internet sample. The discrete choice data were analysed by means of probit and tobit models to estimate the utility consumers attribute to organically grown fruit and to a pesticide ban. The research also addresses questions of validity and representativeness as a fundamental problem in web-based surveys.

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks

Anti-spoofing is attracting growing interest in biometrics, considering the variety of fake materials and new means to attack biometric recognition systems. New unseen materials continuously challenge state-of-the-art spoofing detectors, suggesting for additional systematic approaches to target anti-spoofing. By incorporating liveness scores into the biometric fusion process, recognition accuracy can be enhanced, but traditional sum-rule based fusion algorithms are known to be highly sensitive to single spoofed instances. This paper investigates 1-median filtering as a spoofing-resistant generalised alternative to the sum-rule targeting the problem of partial multibiometric spoofing where m out of n biometric sources to be combined are attacked. Augmenting previous work, this paper investigates the dynamic detection and rejection of livenessrecognition pair outliers for spoofed samples in true multi-modal configuration with its inherent challenge of normalisation. As a further contribution, bootstrap aggregating (bagging) classifiers for fingerprint spoof-detection algorithm is presented. Experiments on the latest face video databases (Idiap Replay- Attack Database and CASIA Face Anti-Spoofing Database), and fingerprint spoofing database (Fingerprint Liveness Detection Competition 2013) illustrate the efficiency of proposed techniques.

Visual search for basic emotional expressions in autism; impaired processing of anger, fear and sadness, but a typical happy face advantage

Facial expression recognition was investigated in 20 males with high functioning autism (HFA) or Asperger syndrome (AS), compared to typically developing individuals matched for chronological age (TD CA group) and verbal and non-verbal ability (TD V/NV group). This was the first study to employ a visual search, “face in the crowd” paradigm with a HFA/AS group, which explored responses to numerous facial expressions using real-face stimuli. Results showed slower response times for processing fear, anger and sad expressions in the HFA/AS group, relative to the TD CA group, but not the TD V/NV group. Reponses to happy, disgust and surprise expressions showed no group differences. Results are discussed with reference to the amygdala theory of autism.

Reduced face preference in infancy: a developmental precursor to callous-unemotional traits?

Background Children with callous-unemotional (CU) traits, a proposed precursor to adult psychopathy, are characterized by impaired emotion recognition, reduced responsiveness to others’ distress, and a lack of guilt or empathy. Reduced attention to faces, and more specifically to the eye region, has been proposed to underlie these difficulties, although this has never been tested longitudinally from infancy. Attention to faces occurs within the context of dyadic caregiver interactions, and early environment including parenting characteristics has been associated with CU traits. The present study tested whether infants’ preferential tracking of a face with direct gaze and levels of maternal sensitivity predict later CU traits. Methods Data were analyzed from a stratified random sample of 213 participants drawn from a population-based sample of 1233 first-time mothers. Infants’ preferential face tracking at 5 weeks and maternal sensitivity at 29 weeks were entered into a weighted linear regression as predictors of CU traits at 2.5 years. Results Controlling for a range of confounders (e.g., deprivation), lower preferential face tracking predicted higher CU traits (p = .001). Higher maternal sensitivity predicted lower CU traits in girls (p = .009), but not boys. No significant interaction between face tracking and maternal sensitivity was found. Conclusions This is the first study to show that attention to social features during infancy as well as early sensitive parenting predict the subsequent development of CU traits. Identifying such early atypicalities offers the potential for developing parent-mediated interventions in children at risk for developing CU traits.